Overview

Brought to you by YData

Dataset statistics

Number of variables124
Number of observations1586
Missing cells14227
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory992.1 B

Variable types

Numeric16
Categorical108

Alerts

AGE is highly overall correlated with KFK_BLOODHigh correlation
ALT_BLOOD is highly overall correlated with AST_BLOOD and 2 other fieldsHigh correlation
ANT_CA_S_n is highly overall correlated with KFK_BLOODHigh correlation
ASP_S_n is highly overall correlated with KFK_BLOODHigh correlation
AST_BLOOD is highly overall correlated with ALT_BLOOD and 2 other fieldsHigh correlation
A_V_BLOK is highly overall correlated with KFK_BLOODHigh correlation
B_BLOK_S_n is highly overall correlated with KFK_BLOODHigh correlation
DLIT_AG is highly overall correlated with GB and 1 other fieldsHigh correlation
DRESSLER is highly overall correlated with KFK_BLOODHigh correlation
D_AD_KBRIG is highly overall correlated with KFK_BLOOD and 8 other fieldsHigh correlation
D_AD_ORIT is highly overall correlated with K_SH_POST and 4 other fieldsHigh correlation
FIBR_JELUD is highly overall correlated with KFK_BLOODHigh correlation
FIBR_PREDS is highly overall correlated with KFK_BLOODHigh correlation
FIB_G_POST is highly overall correlated with KFK_BLOODHigh correlation
FK_STENOK is highly overall correlated with IBS_POST and 2 other fieldsHigh correlation
GB is highly overall correlated with DLIT_AG and 1 other fieldsHigh correlation
GEPAR_S_n is highly overall correlated with KFK_BLOODHigh correlation
GIPER_NA is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
GIPO_K is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
GT_POST is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
IBS_NASL is highly overall correlated with GT_POST and 33 other fieldsHigh correlation
IBS_POST is highly overall correlated with FK_STENOK and 2 other fieldsHigh correlation
ID is highly overall correlated with KFK_BLOOD and 1 other fieldsHigh correlation
IM_PG_P is highly overall correlated with KFK_BLOODHigh correlation
INF_ANAM is highly overall correlated with KFK_BLOODHigh correlation
JELUD_TAH is highly overall correlated with KFK_BLOODHigh correlation
KFK_BLOOD is highly overall correlated with AGE and 119 other fieldsHigh correlation
K_BLOOD is highly overall correlated with GIPO_K and 3 other fieldsHigh correlation
K_SH_POST is highly overall correlated with D_AD_KBRIG and 5 other fieldsHigh correlation
LET_IS is highly overall correlated with ID and 2 other fieldsHigh correlation
LID_KB is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
LID_S_n is highly overall correlated with KFK_BLOODHigh correlation
L_BLOOD is highly overall correlated with KFK_BLOOD and 1 other fieldsHigh correlation
MP_TP_POST is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
NA_BLOOD is highly overall correlated with GIPER_NA and 3 other fieldsHigh correlation
NA_KB is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
NA_R_1_n is highly overall correlated with KFK_BLOODHigh correlation
NA_R_2_n is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
NA_R_3_n is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
NITR_S is highly overall correlated with KFK_BLOODHigh correlation
NOT_NA_1_n is highly overall correlated with KFK_BLOODHigh correlation
NOT_NA_2_n is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
NOT_NA_3_n is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
NOT_NA_KB is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
OTEK_LANC is highly overall correlated with KFK_BLOODHigh correlation
O_L_POST is highly overall correlated with KFK_BLOODHigh correlation
PREDS_TAH is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
P_IM_STEN is highly overall correlated with KFK_BLOODHigh correlation
RAZRIV is highly overall correlated with IBS_NASL and 2 other fieldsHigh correlation
REC_IM is highly overall correlated with KFK_BLOODHigh correlation
ROE is highly overall correlated with KFK_BLOOD and 1 other fieldsHigh correlation
R_AB_1_n is highly overall correlated with KFK_BLOODHigh correlation
R_AB_2_n is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
R_AB_3_n is highly overall correlated with KFK_BLOOD and 3 other fieldsHigh correlation
SEX is highly overall correlated with KFK_BLOODHigh correlation
SIM_GIPERT is highly overall correlated with KFK_BLOODHigh correlation
STENOK_AN is highly overall correlated with FK_STENOK and 2 other fieldsHigh correlation
SVT_POST is highly overall correlated with IBS_NASL and 2 other fieldsHigh correlation
S_AD_KBRIG is highly overall correlated with D_AD_KBRIG and 9 other fieldsHigh correlation
S_AD_ORIT is highly overall correlated with D_AD_ORIT and 5 other fieldsHigh correlation
TIKL_S_n is highly overall correlated with KFK_BLOODHigh correlation
TIME_B_S is highly overall correlated with KFK_BLOODHigh correlation
TRENT_S_n is highly overall correlated with KFK_BLOODHigh correlation
ZSN is highly overall correlated with KFK_BLOODHigh correlation
ZSN_A is highly overall correlated with KFK_BLOODHigh correlation
ant_im is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
endocr_01 is highly overall correlated with KFK_BLOODHigh correlation
endocr_02 is highly overall correlated with KFK_BLOODHigh correlation
endocr_03 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
fibr_ter_01 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
fibr_ter_02 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
fibr_ter_03 is highly overall correlated with KFK_BLOODHigh correlation
fibr_ter_05 is highly overall correlated with D_AD_KBRIG and 6 other fieldsHigh correlation
fibr_ter_06 is highly overall correlated with KFK_BLOODHigh correlation
fibr_ter_07 is highly overall correlated with KFK_BLOODHigh correlation
fibr_ter_08 is highly overall correlated with IBS_NASL and 7 other fieldsHigh correlation
inf_im is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
lat_im is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
n_p_ecg_p_01 is highly overall correlated with D_AD_ORIT and 3 other fieldsHigh correlation
n_p_ecg_p_03 is highly overall correlated with KFK_BLOODHigh correlation
n_p_ecg_p_04 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_p_ecg_p_05 is highly overall correlated with ALT_BLOOD and 19 other fieldsHigh correlation
n_p_ecg_p_06 is highly overall correlated with IBS_NASL and 2 other fieldsHigh correlation
n_p_ecg_p_07 is highly overall correlated with KFK_BLOODHigh correlation
n_p_ecg_p_08 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_p_ecg_p_09 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_p_ecg_p_10 is highly overall correlated with KFK_BLOODHigh correlation
n_p_ecg_p_11 is highly overall correlated with KFK_BLOODHigh correlation
n_p_ecg_p_12 is highly overall correlated with KFK_BLOODHigh correlation
n_r_ecg_p_01 is highly overall correlated with KFK_BLOODHigh correlation
n_r_ecg_p_02 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_r_ecg_p_03 is highly overall correlated with KFK_BLOODHigh correlation
n_r_ecg_p_04 is highly overall correlated with KFK_BLOODHigh correlation
n_r_ecg_p_05 is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
n_r_ecg_p_06 is highly overall correlated with IBS_NASL and 3 other fieldsHigh correlation
n_r_ecg_p_08 is highly overall correlated with IBS_NASL and 2 other fieldsHigh correlation
n_r_ecg_p_09 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_r_ecg_p_10 is highly overall correlated with D_AD_KBRIG and 3 other fieldsHigh correlation
np_01 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
np_04 is highly overall correlated with D_AD_KBRIG and 3 other fieldsHigh correlation
np_05 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
np_07 is highly overall correlated with D_AD_KBRIG and 9 other fieldsHigh correlation
np_08 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
np_09 is highly overall correlated with D_AD_KBRIG and 5 other fieldsHigh correlation
np_10 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
nr_01 is highly overall correlated with KFK_BLOODHigh correlation
nr_02 is highly overall correlated with KFK_BLOODHigh correlation
nr_03 is highly overall correlated with KFK_BLOODHigh correlation
nr_04 is highly overall correlated with IBS_NASL and 3 other fieldsHigh correlation
nr_07 is highly overall correlated with IBS_NASL and 5 other fieldsHigh correlation
nr_08 is highly overall correlated with KFK_BLOODHigh correlation
nr_11 is highly overall correlated with KFK_BLOODHigh correlation
post_im is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
ritm_ecg_p_01 is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
ritm_ecg_p_02 is highly overall correlated with KFK_BLOOD and 5 other fieldsHigh correlation
ritm_ecg_p_04 is highly overall correlated with IBS_NASL and 3 other fieldsHigh correlation
ritm_ecg_p_06 is highly overall correlated with D_AD_KBRIG and 13 other fieldsHigh correlation
ritm_ecg_p_07 is highly overall correlated with KFK_BLOOD and 2 other fieldsHigh correlation
ritm_ecg_p_08 is highly overall correlated with KFK_BLOOD and 1 other fieldsHigh correlation
zab_leg_01 is highly overall correlated with KFK_BLOODHigh correlation
zab_leg_02 is highly overall correlated with KFK_BLOODHigh correlation
zab_leg_03 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
zab_leg_04 is highly overall correlated with KFK_BLOODHigh correlation
zab_leg_06 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
SIM_GIPERT is highly imbalanced (78.5%) Imbalance
ZSN_A is highly imbalanced (71.1%) Imbalance
nr_11 is highly imbalanced (83.0%) Imbalance
nr_01 is highly imbalanced (97.5%) Imbalance
nr_02 is highly imbalanced (90.6%) Imbalance
nr_03 is highly imbalanced (85.4%) Imbalance
nr_04 is highly imbalanced (87.5%) Imbalance
nr_07 is highly imbalanced (99.2%) Imbalance
nr_08 is highly imbalanced (98.0%) Imbalance
np_01 is highly imbalanced (98.6%) Imbalance
np_04 is highly imbalanced (98.6%) Imbalance
np_05 is highly imbalanced (94.0%) Imbalance
np_07 is highly imbalanced (99.2%) Imbalance
np_08 is highly imbalanced (96.4%) Imbalance
np_09 is highly imbalanced (98.6%) Imbalance
np_10 is highly imbalanced (98.0%) Imbalance
endocr_02 is highly imbalanced (83.3%) Imbalance
endocr_03 is highly imbalanced (93.1%) Imbalance
zab_leg_01 is highly imbalanced (59.3%) Imbalance
zab_leg_02 is highly imbalanced (66.0%) Imbalance
zab_leg_03 is highly imbalanced (85.4%) Imbalance
zab_leg_04 is highly imbalanced (94.9%) Imbalance
zab_leg_06 is highly imbalanced (90.2%) Imbalance
O_L_POST is highly imbalanced (67.6%) Imbalance
K_SH_POST is highly imbalanced (86.4%) Imbalance
MP_TP_POST is highly imbalanced (65.6%) Imbalance
SVT_POST is highly imbalanced (95.4%) Imbalance
GT_POST is highly imbalanced (96.4%) Imbalance
FIB_G_POST is highly imbalanced (93.1%) Imbalance
post_im is highly imbalanced (62.4%) Imbalance
IM_PG_P is highly imbalanced (82.3%) Imbalance
ritm_ecg_p_02 is highly imbalanced (67.7%) Imbalance
ritm_ecg_p_04 is highly imbalanced (90.8%) Imbalance
ritm_ecg_p_06 is highly imbalanced (99.2%) Imbalance
ritm_ecg_p_08 is highly imbalanced (81.4%) Imbalance
n_r_ecg_p_01 is highly imbalanced (76.5%) Imbalance
n_r_ecg_p_02 is highly imbalanced (95.7%) Imbalance
n_r_ecg_p_04 is highly imbalanced (74.6%) Imbalance
n_r_ecg_p_05 is highly imbalanced (74.3%) Imbalance
n_r_ecg_p_06 is highly imbalanced (86.5%) Imbalance
n_r_ecg_p_08 is highly imbalanced (97.9%) Imbalance
n_r_ecg_p_09 is highly imbalanced (98.5%) Imbalance
n_r_ecg_p_10 is highly imbalanced (98.5%) Imbalance
n_p_ecg_p_01 is highly imbalanced (99.2%) Imbalance
n_p_ecg_p_03 is highly imbalanced (85.3%) Imbalance
n_p_ecg_p_04 is highly imbalanced (96.7%) Imbalance
n_p_ecg_p_05 is highly imbalanced (99.2%) Imbalance
n_p_ecg_p_06 is highly imbalanced (91.0%) Imbalance
n_p_ecg_p_07 is highly imbalanced (65.6%) Imbalance
n_p_ecg_p_08 is highly imbalanced (95.7%) Imbalance
n_p_ecg_p_09 is highly imbalanced (94.2%) Imbalance
n_p_ecg_p_10 is highly imbalanced (85.0%) Imbalance
n_p_ecg_p_11 is highly imbalanced (87.3%) Imbalance
n_p_ecg_p_12 is highly imbalanced (71.4%) Imbalance
fibr_ter_01 is highly imbalanced (93.1%) Imbalance
fibr_ter_02 is highly imbalanced (91.8%) Imbalance
fibr_ter_03 is highly imbalanced (75.5%) Imbalance
fibr_ter_05 is highly imbalanced (98.0%) Imbalance
fibr_ter_06 is highly imbalanced (95.4%) Imbalance
fibr_ter_07 is highly imbalanced (96.9%) Imbalance
fibr_ter_08 is highly imbalanced (99.2%) Imbalance
GIPER_NA is highly imbalanced (84.0%) Imbalance
R_AB_2_n is highly imbalanced (69.8%) Imbalance
R_AB_3_n is highly imbalanced (80.4%) Imbalance
NA_R_2_n is highly imbalanced (78.5%) Imbalance
NA_R_3_n is highly imbalanced (80.6%) Imbalance
NOT_NA_1_n is highly imbalanced (53.5%) Imbalance
NOT_NA_2_n is highly imbalanced (74.4%) Imbalance
NOT_NA_3_n is highly imbalanced (75.4%) Imbalance
TIKL_S_n is highly imbalanced (86.4%) Imbalance
FIBR_PREDS is highly imbalanced (54.2%) Imbalance
PREDS_TAH is highly imbalanced (91.4%) Imbalance
JELUD_TAH is highly imbalanced (84.0%) Imbalance
FIBR_JELUD is highly imbalanced (75.6%) Imbalance
A_V_BLOK is highly imbalanced (79.8%) Imbalance
OTEK_LANC is highly imbalanced (55.0%) Imbalance
RAZRIV is highly imbalanced (79.8%) Imbalance
DRESSLER is highly imbalanced (74.2%) Imbalance
REC_IM is highly imbalanced (55.9%) Imbalance
P_IM_STEN is highly imbalanced (58.0%) Imbalance
IBS_NASL has 1516 (95.6%) missing values Missing
DLIT_AG has 216 (13.6%) missing values Missing
ZSN_A has 34 (2.1%) missing values Missing
S_AD_KBRIG has 1009 (63.6%) missing values Missing
D_AD_KBRIG has 1009 (63.6%) missing values Missing
S_AD_ORIT has 256 (16.1%) missing values Missing
D_AD_ORIT has 256 (16.1%) missing values Missing
ant_im has 72 (4.5%) missing values Missing
lat_im has 69 (4.4%) missing values Missing
inf_im has 69 (4.4%) missing values Missing
post_im has 63 (4.0%) missing values Missing
ritm_ecg_p_01 has 140 (8.8%) missing values Missing
ritm_ecg_p_02 has 140 (8.8%) missing values Missing
ritm_ecg_p_04 has 140 (8.8%) missing values Missing
ritm_ecg_p_06 has 140 (8.8%) missing values Missing
ritm_ecg_p_07 has 140 (8.8%) missing values Missing
ritm_ecg_p_08 has 140 (8.8%) missing values Missing
n_r_ecg_p_01 has 105 (6.6%) missing values Missing
n_r_ecg_p_02 has 105 (6.6%) missing values Missing
n_r_ecg_p_03 has 105 (6.6%) missing values Missing
n_r_ecg_p_04 has 105 (6.6%) missing values Missing
n_r_ecg_p_05 has 105 (6.6%) missing values Missing
n_r_ecg_p_06 has 105 (6.6%) missing values Missing
n_r_ecg_p_08 has 105 (6.6%) missing values Missing
n_r_ecg_p_09 has 105 (6.6%) missing values Missing
n_r_ecg_p_10 has 105 (6.6%) missing values Missing
n_p_ecg_p_01 has 104 (6.6%) missing values Missing
n_p_ecg_p_03 has 104 (6.6%) missing values Missing
n_p_ecg_p_04 has 104 (6.6%) missing values Missing
n_p_ecg_p_05 has 104 (6.6%) missing values Missing
n_p_ecg_p_06 has 104 (6.6%) missing values Missing
n_p_ecg_p_07 has 104 (6.6%) missing values Missing
n_p_ecg_p_08 has 104 (6.6%) missing values Missing
n_p_ecg_p_09 has 104 (6.6%) missing values Missing
n_p_ecg_p_10 has 104 (6.6%) missing values Missing
n_p_ecg_p_11 has 104 (6.6%) missing values Missing
n_p_ecg_p_12 has 104 (6.6%) missing values Missing
GIPO_K has 341 (21.5%) missing values Missing
K_BLOOD has 342 (21.6%) missing values Missing
GIPER_NA has 345 (21.8%) missing values Missing
NA_BLOOD has 345 (21.8%) missing values Missing
ALT_BLOOD has 262 (16.5%) missing values Missing
AST_BLOOD has 263 (16.6%) missing values Missing
KFK_BLOOD has 1583 (99.8%) missing values Missing
L_BLOOD has 112 (7.1%) missing values Missing
ROE has 187 (11.8%) missing values Missing
TIME_B_S has 119 (7.5%) missing values Missing
R_AB_2_n has 82 (5.2%) missing values Missing
R_AB_3_n has 98 (6.2%) missing values Missing
NA_KB has 615 (38.8%) missing values Missing
NOT_NA_KB has 637 (40.2%) missing values Missing
LID_KB has 629 (39.7%) missing values Missing
NA_R_2_n has 83 (5.2%) missing values Missing
NA_R_3_n has 102 (6.4%) missing values Missing
NOT_NA_2_n has 85 (5.4%) missing values Missing
NOT_NA_3_n has 102 (6.4%) missing values Missing
KFK_BLOOD is uniformly distributed Uniform
ID has unique values Unique
STENOK_AN has 659 (41.6%) zeros Zeros
DLIT_AG has 518 (32.7%) zeros Zeros
LET_IS has 1353 (85.3%) zeros Zeros

Reproduction

Analysis started2024-11-09 15:10:25.277496
Analysis finished2024-11-09 15:12:22.533617
Duration1 minute and 57.26 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Unique 

Distinct1586
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean827.97667
Minimum1
Maximum1699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:22.696372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile81.25
Q1407.25
median817.5
Q31243.75
95-th percentile1608.75
Maximum1699
Range1698
Interquartile range (IQR)836.5

Descriptive statistics

Standard deviation486.76668
Coefficient of variation (CV)0.58789902
Kurtosis-1.1735279
Mean827.97667
Median Absolute Deviation (MAD)418.5
Skewness0.061570234
Sum1313171
Variance236941.8
MonotonicityStrictly increasing
2024-11-09T15:12:23.038846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1699 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (1576) 1576
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1699 1
0.1%
1698 1
0.1%
1697 1
0.1%
1696 1
0.1%
1695 1
0.1%
1694 1
0.1%
1693 1
0.1%
1692 1
0.1%
1691 1
0.1%
1689 1
0.1%

AGE
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.598361
Minimum26
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:23.316852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile42
Q154
median62
Q370
95-th percentile79
Maximum92
Range66
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.24926
Coefficient of variation (CV)0.18262271
Kurtosis-0.20109706
Mean61.598361
Median Absolute Deviation (MAD)8
Skewness-0.22779096
Sum97695
Variance126.54584
MonotonicityNot monotonic
2024-11-09T15:12:23.631860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 80
 
5.0%
65 76
 
4.8%
62 75
 
4.7%
64 61
 
3.8%
70 59
 
3.7%
52 57
 
3.6%
61 52
 
3.3%
67 49
 
3.1%
55 49
 
3.1%
60 49
 
3.1%
Other values (52) 979
61.7%
ValueCountFrequency (%)
26 1
 
0.1%
27 2
 
0.1%
30 1
 
0.1%
32 3
 
0.2%
33 3
 
0.2%
34 6
0.4%
35 5
 
0.3%
36 2
 
0.1%
37 14
0.9%
38 13
0.8%
ValueCountFrequency (%)
92 1
 
0.1%
90 2
 
0.1%
88 4
 
0.3%
87 3
 
0.2%
86 1
 
0.1%
85 5
 
0.3%
84 6
 
0.4%
83 15
0.9%
82 11
0.7%
81 10
0.6%

SEX
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
1
1004 
0
582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

Length

2024-11-09T15:12:23.900065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:24.096666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

Most occurring characters

ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1004
63.3%
0 582
36.7%

INF_ANAM
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
993 
1
381 
2
138 
3
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

Length

2024-11-09T15:12:24.289071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:24.537223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 993
62.6%
1 381
 
24.0%
2 138
 
8.7%
3 74
 
4.7%

STENOK_AN
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3108449
Minimum0
Maximum6
Zeros659
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:24.835463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4399092
Coefficient of variation (CV)1.0558516
Kurtosis-1.4448353
Mean2.3108449
Median Absolute Deviation (MAD)1
Skewness0.47353679
Sum3665
Variance5.9531568
MonotonicityNot monotonic
2024-11-09T15:12:25.211465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 659
41.6%
6 329
20.7%
1 146
 
9.2%
2 137
 
8.6%
5 125
 
7.9%
3 114
 
7.2%
4 76
 
4.8%
ValueCountFrequency (%)
0 659
41.6%
1 146
 
9.2%
2 137
 
8.6%
3 114
 
7.2%
4 76
 
4.8%
5 125
 
7.9%
6 329
20.7%
ValueCountFrequency (%)
6 329
20.7%
5 125
 
7.9%
4 76
 
4.8%
3 114
 
7.2%
2 137
 
8.6%
1 146
 
9.2%
0 659
41.6%

FK_STENOK
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
2.0
810 
0.0
659 
3.0
 
52
1.0
 
47
4.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 810
51.1%
0.0 659
41.6%
3.0 52
 
3.3%
1.0 47
 
3.0%
4.0 11
 
0.7%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:25.670310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:26.005324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 810
51.3%
0.0 659
41.7%
3.0 52
 
3.3%
1.0 47
 
3.0%
4.0 11
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2238
47.2%
. 1579
33.3%
2 810
 
17.1%
3 52
 
1.1%
1 47
 
1.0%
4 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2238
47.2%
. 1579
33.3%
2 810
 
17.1%
3 52
 
1.1%
1 47
 
1.0%
4 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2238
47.2%
. 1579
33.3%
2 810
 
17.1%
3 52
 
1.1%
1 47
 
1.0%
4 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2238
47.2%
. 1579
33.3%
2 810
 
17.1%
3 52
 
1.1%
1 47
 
1.0%
4 11
 
0.2%

IBS_POST
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing15
Missing (%)0.9%
Memory size12.5 KiB
2.0
643 
1.0
515 
0.0
413 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4713
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 643
40.5%
1.0 515
32.5%
0.0 413
26.0%
(Missing) 15
 
0.9%

Length

2024-11-09T15:12:26.471971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:26.828660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 643
40.9%
1.0 515
32.8%
0.0 413
26.3%

Most occurring characters

ValueCountFrequency (%)
0 1984
42.1%
. 1571
33.3%
2 643
 
13.6%
1 515
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1984
42.1%
. 1571
33.3%
2 643
 
13.6%
1 515
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1984
42.1%
. 1571
33.3%
2 643
 
13.6%
1 515
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1984
42.1%
. 1571
33.3%
2 643
 
13.6%
1 515
 
10.9%

IBS_NASL
Categorical

High correlation  Missing 

Distinct2
Distinct (%)2.9%
Missing1516
Missing (%)95.6%
Memory size12.5 KiB
0.0
44 
1.0
26 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters210
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 44
 
2.8%
1.0 26
 
1.6%
(Missing) 1516
95.6%

Length

2024-11-09T15:12:28.038348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:28.574140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44
62.9%
1.0 26
37.1%

Most occurring characters

ValueCountFrequency (%)
0 114
54.3%
. 70
33.3%
1 26
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 114
54.3%
. 70
33.3%
1 26
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 114
54.3%
. 70
33.3%
1 26
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 114
54.3%
. 70
33.3%
1 26
 
12.4%

GB
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
2.0
824 
0.0
570 
3.0
178 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 824
52.0%
0.0 570
35.9%
3.0 178
 
11.2%
1.0 11
 
0.7%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:29.084872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:29.352743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 824
52.1%
0.0 570
36.0%
3.0 178
 
11.2%
1.0 11
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2153
45.3%
. 1583
33.3%
2 824
 
17.4%
3 178
 
3.7%
1 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2153
45.3%
. 1583
33.3%
2 824
 
17.4%
3 178
 
3.7%
1 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2153
45.3%
. 1583
33.3%
2 824
 
17.4%
3 178
 
3.7%
1 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2153
45.3%
. 1583
33.3%
2 824
 
17.4%
3 178
 
3.7%
1 11
 
0.2%

SIM_GIPERT
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1529 
1.0
 
54

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1529
96.4%
1.0 54
 
3.4%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:29.781310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:30.252569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1529
96.6%
1.0 54
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 3112
65.5%
. 1583
33.3%
1 54
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3112
65.5%
. 1583
33.3%
1 54
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3112
65.5%
. 1583
33.3%
1 54
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3112
65.5%
. 1583
33.3%
1 54
 
1.1%

DLIT_AG
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)0.6%
Missing216
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean3.3423358
Minimum0
Maximum7
Zeros518
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:30.674513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.0953256
Coefficient of variation (CV)0.92609654
Kurtosis-1.8200958
Mean3.3423358
Median Absolute Deviation (MAD)3
Skewness0.062292233
Sum4579
Variance9.5810406
MonotonicityNot monotonic
2024-11-09T15:12:30.982520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 518
32.7%
7 405
25.5%
6 160
 
10.1%
1 88
 
5.5%
5 67
 
4.2%
3 57
 
3.6%
2 55
 
3.5%
4 20
 
1.3%
(Missing) 216
13.6%
ValueCountFrequency (%)
0 518
32.7%
1 88
 
5.5%
2 55
 
3.5%
3 57
 
3.6%
4 20
 
1.3%
5 67
 
4.2%
6 160
 
10.1%
7 405
25.5%
ValueCountFrequency (%)
7 405
25.5%
6 160
 
10.1%
5 67
 
4.2%
4 20
 
1.3%
3 57
 
3.6%
2 55
 
3.5%
1 88
 
5.5%
0 518
32.7%

ZSN_A
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.3%
Missing34
Missing (%)2.1%
Memory size12.5 KiB
0.0
1383 
1.0
 
100
2.0
 
26
3.0
 
26
4.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4656
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1383
87.2%
1.0 100
 
6.3%
2.0 26
 
1.6%
3.0 26
 
1.6%
4.0 17
 
1.1%
(Missing) 34
 
2.1%

Length

2024-11-09T15:12:31.296698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:31.638998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1383
89.1%
1.0 100
 
6.4%
2.0 26
 
1.7%
3.0 26
 
1.7%
4.0 17
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2935
63.0%
. 1552
33.3%
1 100
 
2.1%
2 26
 
0.6%
3 26
 
0.6%
4 17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2935
63.0%
. 1552
33.3%
1 100
 
2.1%
2 26
 
0.6%
3 26
 
0.6%
4 17
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2935
63.0%
. 1552
33.3%
1 100
 
2.1%
2 26
 
0.6%
3 26
 
0.6%
4 17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2935
63.0%
. 1552
33.3%
1 100
 
2.1%
2 26
 
0.6%
3 26
 
0.6%
4 17
 
0.4%

nr_11
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1539 
1.0
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1539
97.0%
1.0 40
 
2.5%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:32.073407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:32.570196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1539
97.5%
1.0 40
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 3118
65.8%
. 1579
33.3%
1 40
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3118
65.8%
. 1579
33.3%
1 40
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3118
65.8%
. 1579
33.3%
1 40
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3118
65.8%
. 1579
33.3%
1 40
 
0.8%

nr_01
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1575 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1575
99.3%
1.0 4
 
0.3%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:33.152434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:33.527271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1575
99.7%
1.0 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 3154
66.6%
. 1579
33.3%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3154
66.6%
. 1579
33.3%
1 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3154
66.6%
. 1579
33.3%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3154
66.6%
. 1579
33.3%
1 4
 
0.1%

nr_02
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1560 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1560
98.4%
1.0 19
 
1.2%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:33.766240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:33.982006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1560
98.8%
1.0 19
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 3139
66.3%
. 1579
33.3%
1 19
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3139
66.3%
. 1579
33.3%
1 19
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3139
66.3%
. 1579
33.3%
1 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3139
66.3%
. 1579
33.3%
1 19
 
0.4%

nr_03
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1546 
1.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1546
97.5%
1.0 33
 
2.1%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:34.184629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:34.381551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1546
97.9%
1.0 33
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 3125
66.0%
. 1579
33.3%
1 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3125
66.0%
. 1579
33.3%
1 33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3125
66.0%
. 1579
33.3%
1 33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3125
66.0%
. 1579
33.3%
1 33
 
0.7%

nr_04
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1552 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1552
97.9%
1.0 27
 
1.7%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:34.937136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:35.202677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1552
98.3%
1.0 27
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 3131
66.1%
. 1579
33.3%
1 27
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3131
66.1%
. 1579
33.3%
1 27
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3131
66.1%
. 1579
33.3%
1 27
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3131
66.1%
. 1579
33.3%
1 27
 
0.6%

nr_07
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1578 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1578
99.5%
1.0 1
 
0.1%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:35.404590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:35.609619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1578
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3157
66.6%
. 1579
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3157
66.6%
. 1579
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3157
66.6%
. 1579
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3157
66.6%
. 1579
33.3%
1 1
 
< 0.1%

nr_08
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1576 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1576
99.4%
1.0 3
 
0.2%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:35.832843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:36.051504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1576
99.8%
1.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 3155
66.6%
. 1579
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3155
66.6%
. 1579
33.3%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3155
66.6%
. 1579
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3155
66.6%
. 1579
33.3%
1 3
 
0.1%

np_01
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1580 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1580
99.6%
1.0 2
 
0.1%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:36.261434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:36.460296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1580
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

np_04
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1580 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1580
99.6%
1.0 2
 
0.1%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:36.660254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:36.852524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1580
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

np_05
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1571 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1571
99.1%
1.0 11
 
0.7%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:37.065214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:37.257424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1571
99.3%
1.0 11
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 3153
66.4%
. 1582
33.3%
1 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3153
66.4%
. 1582
33.3%
1 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3153
66.4%
. 1582
33.3%
1 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3153
66.4%
. 1582
33.3%
1 11
 
0.2%

np_07
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1581 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1581
99.7%
1.0 1
 
0.1%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:37.461320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:37.648993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1581
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3163
66.6%
. 1582
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3163
66.6%
. 1582
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3163
66.6%
. 1582
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3163
66.6%
. 1582
33.3%
1 1
 
< 0.1%

np_08
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1576 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1576
99.4%
1.0 6
 
0.4%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:37.851113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:38.033817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1576
99.6%
1.0 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 3158
66.5%
. 1582
33.3%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3158
66.5%
. 1582
33.3%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3158
66.5%
. 1582
33.3%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3158
66.5%
. 1582
33.3%
1 6
 
0.1%

np_09
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1580 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1580
99.6%
1.0 2
 
0.1%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:38.238403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:38.432518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1580
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3162
66.6%
. 1582
33.3%
1 2
 
< 0.1%

np_10
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1579 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1579
99.6%
1.0 3
 
0.2%
(Missing) 4
 
0.3%

Length

2024-11-09T15:12:38.640325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:38.836055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1579
99.8%
1.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 3161
66.6%
. 1582
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3161
66.6%
. 1582
33.3%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3161
66.6%
. 1582
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3161
66.6%
. 1582
33.3%
1 3
 
0.1%

endocr_01
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing7
Missing (%)0.4%
Memory size12.5 KiB
0.0
1362 
1.0
217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4737
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1362
85.9%
1.0 217
 
13.7%
(Missing) 7
 
0.4%

Length

2024-11-09T15:12:39.085669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:39.345867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1362
86.3%
1.0 217
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 2941
62.1%
. 1579
33.3%
1 217
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2941
62.1%
. 1579
33.3%
1 217
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2941
62.1%
. 1579
33.3%
1 217
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2941
62.1%
. 1579
33.3%
1 217
 
4.6%

endocr_02
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing6
Missing (%)0.4%
Memory size12.5 KiB
0.0
1541 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4740
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1541
97.2%
1.0 39
 
2.5%
(Missing) 6
 
0.4%

Length

2024-11-09T15:12:39.649598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:40.037484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1541
97.5%
1.0 39
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 3121
65.8%
. 1580
33.3%
1 39
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3121
65.8%
. 1580
33.3%
1 39
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3121
65.8%
. 1580
33.3%
1 39
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3121
65.8%
. 1580
33.3%
1 39
 
0.8%

endocr_03
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing6
Missing (%)0.4%
Memory size12.5 KiB
0.0
1567 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4740
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1567
98.8%
1.0 13
 
0.8%
(Missing) 6
 
0.4%

Length

2024-11-09T15:12:40.342957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:40.692313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1567
99.2%
1.0 13
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3147
66.4%
. 1580
33.3%
1 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3147
66.4%
. 1580
33.3%
1 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3147
66.4%
. 1580
33.3%
1 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3147
66.4%
. 1580
33.3%
1 13
 
0.3%

zab_leg_01
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1454 
1.0
 
129

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1454
91.7%
1.0 129
 
8.1%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:41.010850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:41.404121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1454
91.9%
1.0 129
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 3037
64.0%
. 1583
33.3%
1 129
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3037
64.0%
. 1583
33.3%
1 129
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3037
64.0%
. 1583
33.3%
1 129
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3037
64.0%
. 1583
33.3%
1 129
 
2.7%

zab_leg_02
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1483 
1.0
 
100

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1483
93.5%
1.0 100
 
6.3%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:41.713650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:42.042114image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1483
93.7%
1.0 100
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 3066
64.6%
. 1583
33.3%
1 100
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3066
64.6%
. 1583
33.3%
1 100
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3066
64.6%
. 1583
33.3%
1 100
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3066
64.6%
. 1583
33.3%
1 100
 
2.1%

zab_leg_03
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1550 
1.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1550
97.7%
1.0 33
 
2.1%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:42.454603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:42.739836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1550
97.9%
1.0 33
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 3133
66.0%
. 1583
33.3%
1 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3133
66.0%
. 1583
33.3%
1 33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3133
66.0%
. 1583
33.3%
1 33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3133
66.0%
. 1583
33.3%
1 33
 
0.7%

zab_leg_04
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1574 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1574
99.2%
1.0 9
 
0.6%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:43.086534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:43.499190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1574
99.4%
1.0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 3157
66.5%
. 1583
33.3%
1 9
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3157
66.5%
. 1583
33.3%
1 9
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3157
66.5%
. 1583
33.3%
1 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3157
66.5%
. 1583
33.3%
1 9
 
0.2%

zab_leg_06
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.2%
Memory size12.5 KiB
0.0
1563 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4749
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1563
98.5%
1.0 20
 
1.3%
(Missing) 3
 
0.2%

Length

2024-11-09T15:12:43.909410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:44.154247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1563
98.7%
1.0 20
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 3146
66.2%
. 1583
33.3%
1 20
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3146
66.2%
. 1583
33.3%
1 20
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3146
66.2%
. 1583
33.3%
1 20
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3146
66.2%
. 1583
33.3%
1 20
 
0.4%

S_AD_KBRIG
Real number (ℝ)

High correlation  Missing 

Distinct30
Distinct (%)5.2%
Missing1009
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean138.05026
Minimum0
Maximum260
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:44.364423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1120
median140
Q3160
95-th percentile200
Maximum260
Range260
Interquartile range (IQR)40

Descriptive statistics

Standard deviation33.961057
Coefficient of variation (CV)0.24600502
Kurtosis1.0231139
Mean138.05026
Median Absolute Deviation (MAD)20
Skewness0.15252381
Sum79655
Variance1153.3534
MonotonicityNot monotonic
2024-11-09T15:12:44.643019image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
140 88
 
5.5%
130 74
 
4.7%
160 72
 
4.5%
120 62
 
3.9%
110 41
 
2.6%
150 38
 
2.4%
170 28
 
1.8%
180 26
 
1.6%
100 24
 
1.5%
90 21
 
1.3%
Other values (20) 103
 
6.5%
(Missing) 1009
63.6%
ValueCountFrequency (%)
0 1
 
0.1%
40 2
 
0.1%
50 2
 
0.1%
60 6
 
0.4%
70 2
 
0.1%
80 21
1.3%
90 21
1.3%
100 24
1.5%
105 2
 
0.1%
110 41
2.6%
ValueCountFrequency (%)
260 2
 
0.1%
240 2
 
0.1%
230 1
 
0.1%
220 8
 
0.5%
210 9
 
0.6%
200 9
 
0.6%
190 13
0.8%
185 1
 
0.1%
180 26
1.6%
170 28
1.8%

D_AD_KBRIG
Real number (ℝ)

High correlation  Missing 

Distinct21
Distinct (%)3.6%
Missing1009
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean82.365685
Minimum0
Maximum190
Zeros4
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:44.867577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q170
median80
Q390
95-th percentile110
Maximum190
Range190
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.434656
Coefficient of variation (CV)0.22381476
Kurtosis5.2672445
Mean82.365685
Median Absolute Deviation (MAD)10
Skewness-0.46926675
Sum47525
Variance339.83653
MonotonicityNot monotonic
2024-11-09T15:12:45.113149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
80 155
 
9.8%
90 150
 
9.5%
100 80
 
5.0%
70 72
 
4.5%
60 56
 
3.5%
120 15
 
0.9%
110 13
 
0.8%
40 10
 
0.6%
85 6
 
0.4%
0 4
 
0.3%
Other values (11) 16
 
1.0%
(Missing) 1009
63.6%
ValueCountFrequency (%)
0 4
 
0.3%
10 1
 
0.1%
20 3
 
0.2%
30 1
 
0.1%
40 10
 
0.6%
45 1
 
0.1%
50 3
 
0.2%
60 56
3.5%
65 2
 
0.1%
70 72
4.5%
ValueCountFrequency (%)
190 1
 
0.1%
160 1
 
0.1%
140 1
 
0.1%
120 15
 
0.9%
110 13
 
0.8%
100 80
5.0%
95 1
 
0.1%
90 150
9.5%
85 6
 
0.4%
80 155
9.8%

S_AD_ORIT
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)2.3%
Missing256
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean135.83835
Minimum0
Maximum260
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:45.359860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1120
median130
Q3150
95-th percentile190
Maximum260
Range260
Interquartile range (IQR)30

Descriptive statistics

Standard deviation29.46718
Coefficient of variation (CV)0.21692829
Kurtosis1.5806535
Mean135.83835
Median Absolute Deviation (MAD)20
Skewness0.031528593
Sum180665
Variance868.31471
MonotonicityNot monotonic
2024-11-09T15:12:45.648468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
130 233
14.7%
120 210
13.2%
140 203
12.8%
160 145
9.1%
110 113
7.1%
150 91
 
5.7%
180 62
 
3.9%
170 50
 
3.2%
100 46
 
2.9%
90 31
 
2.0%
Other values (21) 146
9.2%
(Missing) 256
16.1%
ValueCountFrequency (%)
0 3
 
0.2%
40 2
 
0.1%
50 2
 
0.1%
60 15
 
0.9%
70 9
 
0.6%
80 21
1.3%
90 31
2.0%
95 2
 
0.1%
100 46
2.9%
105 4
 
0.3%
ValueCountFrequency (%)
260 1
 
0.1%
240 1
 
0.1%
230 2
 
0.1%
220 8
 
0.5%
210 6
 
0.4%
200 26
1.6%
195 1
 
0.1%
190 25
1.6%
180 62
3.9%
170 50
3.2%

D_AD_ORIT
Real number (ℝ)

High correlation  Missing 

Distinct19
Distinct (%)1.4%
Missing256
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean83.744361
Minimum0
Maximum190
Zeros8
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:12:45.875283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q180
median80
Q390
95-th percentile110
Maximum190
Range190
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.62794
Coefficient of variation (CV)0.19855594
Kurtosis5.1261413
Mean83.744361
Median Absolute Deviation (MAD)10
Skewness-0.55241963
Sum111380
Variance276.4884
MonotonicityNot monotonic
2024-11-09T15:12:46.109263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
80 476
30.0%
90 285
18.0%
100 194
12.2%
70 154
 
9.7%
60 70
 
4.4%
110 53
 
3.3%
40 24
 
1.5%
120 22
 
1.4%
50 15
 
0.9%
0 8
 
0.5%
Other values (9) 29
 
1.8%
(Missing) 256
16.1%
ValueCountFrequency (%)
0 8
 
0.5%
20 1
 
0.1%
40 24
 
1.5%
50 15
 
0.9%
60 70
 
4.4%
65 1
 
0.1%
70 154
 
9.7%
75 3
 
0.2%
80 476
30.0%
85 4
 
0.3%
ValueCountFrequency (%)
190 1
 
0.1%
140 4
 
0.3%
130 7
 
0.4%
120 22
 
1.4%
110 53
 
3.3%
105 2
 
0.1%
100 194
12.2%
95 6
 
0.4%
90 285
18.0%
85 4
 
0.3%

O_L_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.7%
Memory size12.5 KiB
0.0
1482 
1.0
 
93

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1482
93.4%
1.0 93
 
5.9%
(Missing) 11
 
0.7%

Length

2024-11-09T15:12:46.359730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:46.560390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1482
94.1%
1.0 93
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 3057
64.7%
. 1575
33.3%
1 93
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3057
64.7%
. 1575
33.3%
1 93
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3057
64.7%
. 1575
33.3%
1 93
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3057
64.7%
. 1575
33.3%
1 93
 
2.0%

K_SH_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing14
Missing (%)0.9%
Memory size12.5 KiB
0.0
1542 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4716
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1542
97.2%
1.0 30
 
1.9%
(Missing) 14
 
0.9%

Length

2024-11-09T15:12:46.775065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:46.968150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1542
98.1%
1.0 30
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

MP_TP_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing13
Missing (%)0.8%
Memory size12.5 KiB
0.0
1472 
1.0
 
101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4719
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1472
92.8%
1.0 101
 
6.4%
(Missing) 13
 
0.8%

Length

2024-11-09T15:12:47.176089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:47.368240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1472
93.6%
1.0 101
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 3045
64.5%
. 1573
33.3%
1 101
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3045
64.5%
. 1573
33.3%
1 101
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3045
64.5%
. 1573
33.3%
1 101
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3045
64.5%
. 1573
33.3%
1 101
 
2.1%

SVT_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.7%
Memory size12.5 KiB
0.0
1567 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1567
98.8%
1.0 8
 
0.5%
(Missing) 11
 
0.7%

Length

2024-11-09T15:12:47.608744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:47.822610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1567
99.5%
1.0 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 3142
66.5%
. 1575
33.3%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3142
66.5%
. 1575
33.3%
1 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3142
66.5%
. 1575
33.3%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3142
66.5%
. 1575
33.3%
1 8
 
0.2%

GT_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.7%
Memory size12.5 KiB
0.0
1569 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1569
98.9%
1.0 6
 
0.4%
(Missing) 11
 
0.7%

Length

2024-11-09T15:12:48.030991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:48.215345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1569
99.6%
1.0 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 3144
66.5%
. 1575
33.3%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1575
33.3%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1575
33.3%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1575
33.3%
1 6
 
0.1%

FIB_G_POST
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.7%
Memory size12.5 KiB
0.0
1562 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1562
98.5%
1.0 13
 
0.8%
(Missing) 11
 
0.7%

Length

2024-11-09T15:12:48.418947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:48.633415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1562
99.2%
1.0 13
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3137
66.4%
. 1575
33.3%
1 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3137
66.4%
. 1575
33.3%
1 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3137
66.4%
. 1575
33.3%
1 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3137
66.4%
. 1575
33.3%
1 13
 
0.3%

ant_im
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.3%
Missing72
Missing (%)4.5%
Memory size12.5 KiB
0.0
617 
4.0
457 
1.0
369 
2.0
 
38
3.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4542
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row4.0
4th row0.0
5th row4.0

Common Values

ValueCountFrequency (%)
0.0 617
38.9%
4.0 457
28.8%
1.0 369
23.3%
2.0 38
 
2.4%
3.0 33
 
2.1%
(Missing) 72
 
4.5%

Length

2024-11-09T15:12:48.842886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:49.068015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 617
40.8%
4.0 457
30.2%
1.0 369
24.4%
2.0 38
 
2.5%
3.0 33
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 2131
46.9%
. 1514
33.3%
4 457
 
10.1%
1 369
 
8.1%
2 38
 
0.8%
3 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2131
46.9%
. 1514
33.3%
4 457
 
10.1%
1 369
 
8.1%
2 38
 
0.8%
3 33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2131
46.9%
. 1514
33.3%
4 457
 
10.1%
1 369
 
8.1%
2 38
 
0.8%
3 33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2131
46.9%
. 1514
33.3%
4 457
 
10.1%
1 369
 
8.1%
2 38
 
0.8%
3 33
 
0.7%

lat_im
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.3%
Missing69
Missing (%)4.4%
Memory size12.5 KiB
1.0
788 
0.0
538 
2.0
90 
3.0
 
67
4.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4551
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 788
49.7%
0.0 538
33.9%
2.0 90
 
5.7%
3.0 67
 
4.2%
4.0 34
 
2.1%
(Missing) 69
 
4.4%

Length

2024-11-09T15:12:49.305838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:49.545190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 788
51.9%
0.0 538
35.5%
2.0 90
 
5.9%
3.0 67
 
4.4%
4.0 34
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 2055
45.2%
. 1517
33.3%
1 788
 
17.3%
2 90
 
2.0%
3 67
 
1.5%
4 34
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2055
45.2%
. 1517
33.3%
1 788
 
17.3%
2 90
 
2.0%
3 67
 
1.5%
4 34
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2055
45.2%
. 1517
33.3%
1 788
 
17.3%
2 90
 
2.0%
3 67
 
1.5%
4 34
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2055
45.2%
. 1517
33.3%
1 788
 
17.3%
2 90
 
2.0%
3 67
 
1.5%
4 34
 
0.7%

inf_im
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.3%
Missing69
Missing (%)4.4%
Memory size12.5 KiB
0.0
879 
1.0
185 
2.0
176 
4.0
164 
3.0
113 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4551
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 879
55.4%
1.0 185
 
11.7%
2.0 176
 
11.1%
4.0 164
 
10.3%
3.0 113
 
7.1%
(Missing) 69
 
4.4%

Length

2024-11-09T15:12:49.806829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:50.024651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 879
57.9%
1.0 185
 
12.2%
2.0 176
 
11.6%
4.0 164
 
10.8%
3.0 113
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2396
52.6%
. 1517
33.3%
1 185
 
4.1%
2 176
 
3.9%
4 164
 
3.6%
3 113
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2396
52.6%
. 1517
33.3%
1 185
 
4.1%
2 176
 
3.9%
4 164
 
3.6%
3 113
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2396
52.6%
. 1517
33.3%
1 185
 
4.1%
2 176
 
3.9%
4 164
 
3.6%
3 113
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2396
52.6%
. 1517
33.3%
1 185
 
4.1%
2 176
 
3.9%
4 164
 
3.6%
3 113
 
2.5%

post_im
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.3%
Missing63
Missing (%)4.0%
Memory size12.5 KiB
0.0
1280 
1.0
150 
2.0
 
48
3.0
 
33
4.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4569
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1280
80.7%
1.0 150
 
9.5%
2.0 48
 
3.0%
3.0 33
 
2.1%
4.0 12
 
0.8%
(Missing) 63
 
4.0%

Length

2024-11-09T15:12:50.269360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:50.493633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1280
84.0%
1.0 150
 
9.8%
2.0 48
 
3.2%
3.0 33
 
2.2%
4.0 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2803
61.3%
. 1523
33.3%
1 150
 
3.3%
2 48
 
1.1%
3 33
 
0.7%
4 12
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2803
61.3%
. 1523
33.3%
1 150
 
3.3%
2 48
 
1.1%
3 33
 
0.7%
4 12
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2803
61.3%
. 1523
33.3%
1 150
 
3.3%
2 48
 
1.1%
3 33
 
0.7%
4 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2803
61.3%
. 1523
33.3%
1 150
 
3.3%
2 48
 
1.1%
3 33
 
0.7%
4 12
 
0.3%

IM_PG_P
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size12.5 KiB
0.0
1543 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4755
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1543
97.3%
1.0 42
 
2.6%
(Missing) 1
 
0.1%

Length

2024-11-09T15:12:50.738191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:50.935144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1543
97.4%
1.0 42
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 3128
65.8%
. 1585
33.3%
1 42
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3128
65.8%
. 1585
33.3%
1 42
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3128
65.8%
. 1585
33.3%
1 42
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3128
65.8%
. 1585
33.3%
1 42
 
0.9%

ritm_ecg_p_01
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
1.0
975 
0.0
471 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 975
61.5%
0.0 471
29.7%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:51.136094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:51.332419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 975
67.4%
0.0 471
32.6%

Most occurring characters

ValueCountFrequency (%)
0 1917
44.2%
. 1446
33.3%
1 975
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1917
44.2%
. 1446
33.3%
1 975
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1917
44.2%
. 1446
33.3%
1 975
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1917
44.2%
. 1446
33.3%
1 975
22.5%

ritm_ecg_p_02
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
0.0
1361 
1.0
 
85

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1361
85.8%
1.0 85
 
5.4%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:51.551141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:51.761968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1361
94.1%
1.0 85
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 2807
64.7%
. 1446
33.3%
1 85
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2807
64.7%
. 1446
33.3%
1 85
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2807
64.7%
. 1446
33.3%
1 85
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2807
64.7%
. 1446
33.3%
1 85
 
2.0%

ritm_ecg_p_04
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
0.0
1429 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1429
90.1%
1.0 17
 
1.1%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:51.961170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:52.153386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1429
98.8%
1.0 17
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2875
66.3%
. 1446
33.3%
1 17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2875
66.3%
. 1446
33.3%
1 17
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2875
66.3%
. 1446
33.3%
1 17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2875
66.3%
. 1446
33.3%
1 17
 
0.4%

ritm_ecg_p_06
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
0.0
1445 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1445
91.1%
1.0 1
 
0.1%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:52.466102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:52.678828image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1445
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2891
66.6%
. 1446
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2891
66.6%
. 1446
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2891
66.6%
. 1446
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2891
66.6%
. 1446
33.3%
1 1
 
< 0.1%

ritm_ecg_p_07
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
0.0
1119 
1.0
327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1119
70.6%
1.0 327
 
20.6%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:52.909490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:53.106626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1119
77.4%
1.0 327
 
22.6%

Most occurring characters

ValueCountFrequency (%)
0 2565
59.1%
. 1446
33.3%
1 327
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2565
59.1%
. 1446
33.3%
1 327
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2565
59.1%
. 1446
33.3%
1 327
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2565
59.1%
. 1446
33.3%
1 327
 
7.5%

ritm_ecg_p_08
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing140
Missing (%)8.8%
Memory size12.5 KiB
0.0
1405 
1.0
 
41

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4338
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1405
88.6%
1.0 41
 
2.6%
(Missing) 140
 
8.8%

Length

2024-11-09T15:12:53.321540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:53.511591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1405
97.2%
1.0 41
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 2851
65.7%
. 1446
33.3%
1 41
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2851
65.7%
. 1446
33.3%
1 41
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2851
65.7%
. 1446
33.3%
1 41
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2851
65.7%
. 1446
33.3%
1 41
 
0.9%

n_r_ecg_p_01
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1424 
1.0
 
57

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1424
89.8%
1.0 57
 
3.6%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:53.712970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:53.924439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1424
96.2%
1.0 57
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 2905
65.4%
. 1481
33.3%
1 57
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2905
65.4%
. 1481
33.3%
1 57
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2905
65.4%
. 1481
33.3%
1 57
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2905
65.4%
. 1481
33.3%
1 57
 
1.3%

n_r_ecg_p_02
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1474 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1474
92.9%
1.0 7
 
0.4%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:54.245459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:54.729360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1474
99.5%
1.0 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2955
66.5%
. 1481
33.3%
1 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2955
66.5%
. 1481
33.3%
1 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2955
66.5%
. 1481
33.3%
1 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2955
66.5%
. 1481
33.3%
1 7
 
0.2%

n_r_ecg_p_03
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1285 
1.0
196 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1285
81.0%
1.0 196
 
12.4%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:55.580440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:56.143357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1285
86.8%
1.0 196
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 2766
62.3%
. 1481
33.3%
1 196
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2766
62.3%
. 1481
33.3%
1 196
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2766
62.3%
. 1481
33.3%
1 196
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2766
62.3%
. 1481
33.3%
1 196
 
4.4%

n_r_ecg_p_04
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1418 
1.0
 
63

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1418
89.4%
1.0 63
 
4.0%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:56.494373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:56.885799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1418
95.7%
1.0 63
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 2899
65.2%
. 1481
33.3%
1 63
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2899
65.2%
. 1481
33.3%
1 63
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2899
65.2%
. 1481
33.3%
1 63
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2899
65.2%
. 1481
33.3%
1 63
 
1.4%

n_r_ecg_p_05
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1417 
1.0
 
64

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1417
89.3%
1.0 64
 
4.0%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:57.299530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:57.679309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1417
95.7%
1.0 64
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 2898
65.2%
. 1481
33.3%
1 64
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2898
65.2%
. 1481
33.3%
1 64
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2898
65.2%
. 1481
33.3%
1 64
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2898
65.2%
. 1481
33.3%
1 64
 
1.4%

n_r_ecg_p_06
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1453 
1.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1453
91.6%
1.0 28
 
1.8%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:58.116544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:58.525755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1453
98.1%
1.0 28
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 2934
66.0%
. 1481
33.3%
1 28
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2934
66.0%
. 1481
33.3%
1 28
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2934
66.0%
. 1481
33.3%
1 28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2934
66.0%
. 1481
33.3%
1 28
 
0.6%

n_r_ecg_p_08
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1478 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1478
93.2%
1.0 3
 
0.2%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:58.991577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:59.226724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1478
99.8%
1.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2959
66.6%
. 1481
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1481
33.3%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1481
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1481
33.3%
1 3
 
0.1%

n_r_ecg_p_09
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1479 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1479
93.3%
1.0 2
 
0.1%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:59.442676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:12:59.646178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1479
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

n_r_ecg_p_10
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing105
Missing (%)6.6%
Memory size12.5 KiB
0.0
1479 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4443
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1479
93.3%
1.0 2
 
0.1%
(Missing) 105
 
6.6%

Length

2024-11-09T15:12:59.861105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:00.089011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1479
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4443
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2960
66.6%
. 1481
33.3%
1 2
 
< 0.1%

n_p_ecg_p_01
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1481 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1481
93.4%
1.0 1
 
0.1%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:00.313375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:00.505011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1481
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

n_p_ecg_p_03
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1451 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1451
91.5%
1.0 31
 
2.0%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:00.703091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:00.899730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1451
97.9%
1.0 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 2933
66.0%
. 1482
33.3%
1 31
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2933
66.0%
. 1482
33.3%
1 31
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2933
66.0%
. 1482
33.3%
1 31
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2933
66.0%
. 1482
33.3%
1 31
 
0.7%

n_p_ecg_p_04
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1477 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1477
93.1%
1.0 5
 
0.3%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:01.103530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:01.317547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1477
99.7%
1.0 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2959
66.6%
. 1482
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1482
33.3%
1 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1482
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2959
66.6%
. 1482
33.3%
1 5
 
0.1%

n_p_ecg_p_05
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1481 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1481
93.4%
1.0 1
 
0.1%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:02.284788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:02.478438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1481
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2963
66.6%
. 1482
33.3%
1 1
 
< 0.1%

n_p_ecg_p_06
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1465 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1465
92.4%
1.0 17
 
1.1%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:02.691564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:02.891067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1465
98.9%
1.0 17
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 2947
66.3%
. 1482
33.3%
1 17
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2947
66.3%
. 1482
33.3%
1 17
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2947
66.3%
. 1482
33.3%
1 17
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2947
66.3%
. 1482
33.3%
1 17
 
0.4%

n_p_ecg_p_07
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1387 
1.0
 
95

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1387
87.5%
1.0 95
 
6.0%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:03.097845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:03.289832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1387
93.6%
1.0 95
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2869
64.5%
. 1482
33.3%
1 95
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2869
64.5%
. 1482
33.3%
1 95
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2869
64.5%
. 1482
33.3%
1 95
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2869
64.5%
. 1482
33.3%
1 95
 
2.1%

n_p_ecg_p_08
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1475 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1475
93.0%
1.0 7
 
0.4%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:03.501039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:03.693156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1475
99.5%
1.0 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2957
66.5%
. 1482
33.3%
1 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2957
66.5%
. 1482
33.3%
1 7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2957
66.5%
. 1482
33.3%
1 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2957
66.5%
. 1482
33.3%
1 7
 
0.2%

n_p_ecg_p_09
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1472 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1472
92.8%
1.0 10
 
0.6%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:03.901522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:04.090786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1472
99.3%
1.0 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2954
66.4%
. 1482
33.3%
1 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2954
66.4%
. 1482
33.3%
1 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2954
66.4%
. 1482
33.3%
1 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2954
66.4%
. 1482
33.3%
1 10
 
0.2%

n_p_ecg_p_10
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1450 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1450
91.4%
1.0 32
 
2.0%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:04.326118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:04.515453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1450
97.8%
1.0 32
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 2932
65.9%
. 1482
33.3%
1 32
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2932
65.9%
. 1482
33.3%
1 32
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2932
65.9%
. 1482
33.3%
1 32
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2932
65.9%
. 1482
33.3%
1 32
 
0.7%

n_p_ecg_p_11
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1456 
1.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1456
91.8%
1.0 26
 
1.6%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:04.717633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:04.908880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1456
98.2%
1.0 26
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 2938
66.1%
. 1482
33.3%
1 26
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2938
66.1%
. 1482
33.3%
1 26
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2938
66.1%
. 1482
33.3%
1 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2938
66.1%
. 1482
33.3%
1 26
 
0.6%

n_p_ecg_p_12
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing104
Missing (%)6.6%
Memory size12.5 KiB
0.0
1408 
1.0
 
74

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4446
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1408
88.8%
1.0 74
 
4.7%
(Missing) 104
 
6.6%

Length

2024-11-09T15:13:05.114344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:05.326497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1408
95.0%
1.0 74
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 2890
65.0%
. 1482
33.3%
1 74
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2890
65.0%
. 1482
33.3%
1 74
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2890
65.0%
. 1482
33.3%
1 74
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2890
65.0%
. 1482
33.3%
1 74
 
1.7%

fibr_ter_01
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1563 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1563
98.5%
1.0 13
 
0.8%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:05.528855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:05.717229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1563
99.2%
1.0 13
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3139
66.4%
. 1576
33.3%
1 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3139
66.4%
. 1576
33.3%
1 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3139
66.4%
. 1576
33.3%
1 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3139
66.4%
. 1576
33.3%
1 13
 
0.3%

fibr_ter_02
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1560 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1560
98.4%
1.0 16
 
1.0%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:05.930410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:06.117434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1560
99.0%
1.0 16
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 3136
66.3%
. 1576
33.3%
1 16
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3136
66.3%
. 1576
33.3%
1 16
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3136
66.3%
. 1576
33.3%
1 16
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3136
66.3%
. 1576
33.3%
1 16
 
0.3%

fibr_ter_03
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1512 
1.0
 
64

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1512
95.3%
1.0 64
 
4.0%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:06.332600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:06.530764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1512
95.9%
1.0 64
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 3088
65.3%
. 1576
33.3%
1 64
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3088
65.3%
. 1576
33.3%
1 64
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3088
65.3%
. 1576
33.3%
1 64
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3088
65.3%
. 1576
33.3%
1 64
 
1.4%

fibr_ter_05
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1573 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1573
99.2%
1.0 3
 
0.2%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:06.732538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:06.925604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1573
99.8%
1.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 3149
66.6%
. 1576
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3149
66.6%
. 1576
33.3%
1 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3149
66.6%
. 1576
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3149
66.6%
. 1576
33.3%
1 3
 
0.1%

fibr_ter_06
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1568 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1568
98.9%
1.0 8
 
0.5%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:07.130588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:07.331806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1568
99.5%
1.0 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 3144
66.5%
. 1576
33.3%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1576
33.3%
1 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1576
33.3%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3144
66.5%
. 1576
33.3%
1 8
 
0.2%

fibr_ter_07
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1571 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1571
99.1%
1.0 5
 
0.3%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:07.552853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:07.752185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1571
99.7%
1.0 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 3147
66.6%
. 1576
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3147
66.6%
. 1576
33.3%
1 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3147
66.6%
. 1576
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3147
66.6%
. 1576
33.3%
1 5
 
0.1%

fibr_ter_08
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1575 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1575
99.3%
1.0 1
 
0.1%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:07.956342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:08.145850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1575
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 3151
66.6%
. 1576
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3151
66.6%
. 1576
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3151
66.6%
. 1576
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3151
66.6%
. 1576
33.3%
1 1
 
< 0.1%

GIPO_K
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing341
Missing (%)21.5%
Memory size12.5 KiB
0.0
742 
1.0
503 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3735
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 742
46.8%
1.0 503
31.7%
(Missing) 341
21.5%

Length

2024-11-09T15:13:08.342596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:08.544204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 742
59.6%
1.0 503
40.4%

Most occurring characters

ValueCountFrequency (%)
0 1987
53.2%
. 1245
33.3%
1 503
 
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1987
53.2%
. 1245
33.3%
1 503
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1987
53.2%
. 1245
33.3%
1 503
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1987
53.2%
. 1245
33.3%
1 503
 
13.5%

K_BLOOD
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)4.1%
Missing342
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean4.1917203
Minimum2.3
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:08.761580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.1
Q13.7
median4.1
Q34.6
95-th percentile5.5
Maximum8.2
Range5.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.75675808
Coefficient of variation (CV)0.1805364
Kurtosis2.5666355
Mean4.1917203
Median Absolute Deviation (MAD)0.5
Skewness0.98547343
Sum5214.5
Variance0.5726828
MonotonicityNot monotonic
2024-11-09T15:13:09.053548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 94
 
5.9%
3.8 90
 
5.7%
4.2 81
 
5.1%
3.9 75
 
4.7%
3.5 64
 
4.0%
4.1 57
 
3.6%
4.5 57
 
3.6%
4.3 57
 
3.6%
4.7 54
 
3.4%
3.6 54
 
3.4%
Other values (41) 561
35.4%
(Missing) 342
21.6%
ValueCountFrequency (%)
2.3 4
 
0.3%
2.4 3
 
0.2%
2.5 1
 
0.1%
2.7 4
 
0.3%
2.8 3
 
0.2%
2.9 6
 
0.4%
3 22
1.4%
3.1 27
1.7%
3.2 29
1.8%
3.3 32
2.0%
ValueCountFrequency (%)
8.2 1
0.1%
8 1
0.1%
7.8 1
0.1%
7.7 1
0.1%
7.6 1
0.1%
7.3 1
0.1%
7.2 1
0.1%
6.9 2
0.1%
6.8 2
0.1%
6.7 2
0.1%

GIPER_NA
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.2%
Missing345
Missing (%)21.8%
Memory size12.5 KiB
0.0
1212 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3723
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1212
76.4%
1.0 29
 
1.8%
(Missing) 345
 
21.8%

Length

2024-11-09T15:13:09.562472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:09.957948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1212
97.7%
1.0 29
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 2453
65.9%
. 1241
33.3%
1 29
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2453
65.9%
. 1241
33.3%
1 29
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2453
65.9%
. 1241
33.3%
1 29
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2453
65.9%
. 1241
33.3%
1 29
 
0.8%

NA_BLOOD
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)3.2%
Missing345
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean136.51249
Minimum117
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:10.279974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile126
Q1133
median136
Q3140
95-th percentile146
Maximum169
Range52
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.5081869
Coefficient of variation (CV)0.047674663
Kurtosis1.3236219
Mean136.51249
Median Absolute Deviation (MAD)4
Skewness0.16877389
Sum169412
Variance42.356497
MonotonicityNot monotonic
2024-11-09T15:13:10.776072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
136 199
12.5%
140 177
11.2%
130 116
 
7.3%
133 79
 
5.0%
138 76
 
4.8%
143 58
 
3.7%
134 56
 
3.5%
132 55
 
3.5%
146 53
 
3.3%
139 46
 
2.9%
Other values (30) 326
20.6%
(Missing) 345
21.8%
ValueCountFrequency (%)
117 6
 
0.4%
118 1
 
0.1%
120 10
0.6%
121 4
 
0.3%
122 4
 
0.3%
123 14
0.9%
124 10
0.6%
125 13
0.8%
126 10
0.6%
127 21
1.3%
ValueCountFrequency (%)
169 1
 
0.1%
163 1
 
0.1%
159 4
 
0.3%
156 4
 
0.3%
154 2
 
0.1%
153 14
0.9%
151 1
 
0.1%
150 17
1.1%
149 2
 
0.1%
148 2
 
0.1%

ALT_BLOOD
Real number (ℝ)

High correlation  Missing 

Distinct69
Distinct (%)5.2%
Missing262
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean0.48305891
Minimum0.03
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:11.222327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.15
Q10.23
median0.38
Q30.61
95-th percentile1.348
Maximum3
Range2.97
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.39226874
Coefficient of variation (CV)0.81205155
Kurtosis6.9470542
Mean0.48305891
Median Absolute Deviation (MAD)0.15
Skewness2.2790381
Sum639.57
Variance0.15387476
MonotonicityNot monotonic
2024-11-09T15:13:11.786178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 218
13.7%
0.3 195
12.3%
0.45 168
10.6%
0.23 137
8.6%
0.38 116
7.3%
0.61 72
 
4.5%
0.75 64
 
4.0%
0.52 59
 
3.7%
0.9 33
 
2.1%
1.05 27
 
1.7%
Other values (59) 235
14.8%
(Missing) 262
16.5%
ValueCountFrequency (%)
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 1
 
0.1%
0.07 20
 
1.3%
0.08 1
 
0.1%
0.09 1
 
0.1%
0.11 3
 
0.2%
0.14 8
 
0.5%
0.15 218
13.7%
0.18 4
 
0.3%
ValueCountFrequency (%)
3 1
 
0.1%
2.86 1
 
0.1%
2.72 1
 
0.1%
2.56 1
 
0.1%
2.4 2
 
0.1%
2.26 3
0.2%
2.12 1
 
0.1%
2.1 4
0.3%
1.96 5
0.3%
1.89 2
 
0.1%

AST_BLOOD
Real number (ℝ)

High correlation  Missing 

Distinct54
Distinct (%)4.1%
Missing263
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean0.26213908
Minimum0.04
Maximum2.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:12.312672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.07
Q10.15
median0.22
Q30.33
95-th percentile0.67
Maximum2.15
Range2.11
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.20065597
Coefficient of variation (CV)0.76545616
Kurtosis12.090447
Mean0.26213908
Median Absolute Deviation (MAD)0.08
Skewness2.6139291
Sum346.81
Variance0.040262819
MonotonicityNot monotonic
2024-11-09T15:13:12.785124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 257
16.2%
0.22 161
10.2%
0.07 143
9.0%
0.3 132
8.3%
0.18 105
 
6.6%
0.11 102
 
6.4%
0.37 77
 
4.9%
0.45 58
 
3.7%
0.26 50
 
3.2%
0.52 29
 
1.8%
Other values (44) 209
13.2%
(Missing) 263
16.6%
ValueCountFrequency (%)
0.04 15
 
0.9%
0.07 143
9.0%
0.08 4
 
0.3%
0.1 2
 
0.1%
0.11 102
 
6.4%
0.13 1
 
0.1%
0.14 4
 
0.3%
0.15 257
16.2%
0.18 105
6.6%
0.2 2
 
0.1%
ValueCountFrequency (%)
2.15 1
 
0.1%
1.75 1
 
0.1%
1.36 1
 
0.1%
1.34 3
0.2%
1.13 1
 
0.1%
1.12 2
0.1%
1.08 1
 
0.1%
1.05 1
 
0.1%
1.04 3
0.2%
0.97 1
 
0.1%

KFK_BLOOD
Categorical

High correlation  Missing  Uniform 

Distinct3
Distinct (%)100.0%
Missing1583
Missing (%)99.8%
Memory size12.5 KiB
1.8
1.4
3.6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row1.8
2nd row1.4
3rd row3.6

Common Values

ValueCountFrequency (%)
1.8 1
 
0.1%
1.4 1
 
0.1%
3.6 1
 
0.1%
(Missing) 1583
99.8%

Length

2024-11-09T15:13:13.245619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:13.810353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.8 1
33.3%
1.4 1
33.3%
3.6 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
33.3%
1 2
22.2%
8 1
 
11.1%
4 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3
33.3%
1 2
22.2%
8 1
 
11.1%
4 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3
33.3%
1 2
22.2%
8 1
 
11.1%
4 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3
33.3%
1 2
22.2%
8 1
 
11.1%
4 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

L_BLOOD
Real number (ℝ)

High correlation  Missing 

Distinct172
Distinct (%)11.7%
Missing112
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean8.7640366
Minimum2
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:14.228451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.665
Q16.4
median8
Q310.4
95-th percentile15.335
Maximum27.9
Range25.9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.381627
Coefficient of variation (CV)0.38585267
Kurtosis2.7304988
Mean8.7640366
Median Absolute Deviation (MAD)1.9
Skewness1.3422246
Sum12918.19
Variance11.435401
MonotonicityNot monotonic
2024-11-09T15:13:14.520673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9 31
 
2.0%
8 29
 
1.8%
7 27
 
1.7%
7.4 25
 
1.6%
6.8 25
 
1.6%
7.7 25
 
1.6%
6 24
 
1.5%
8.9 24
 
1.5%
7.3 24
 
1.5%
5 24
 
1.5%
Other values (162) 1216
76.7%
(Missing) 112
 
7.1%
ValueCountFrequency (%)
2 1
 
0.1%
2.1 1
 
0.1%
2.9 1
 
0.1%
3.2 2
0.1%
3.4 1
 
0.1%
3.5 1
 
0.1%
3.6 1
 
0.1%
3.7 1
 
0.1%
3.8 2
0.1%
3.9 3
0.2%
ValueCountFrequency (%)
27.9 1
0.1%
25 1
0.1%
24.9 1
0.1%
23.5 1
0.1%
22.9 1
0.1%
22.6 1
0.1%
22.4 1
0.1%
22.1 1
0.1%
22 1
0.1%
21.9 1
0.1%

ROE
Real number (ℝ)

High correlation  Missing 

Distinct58
Distinct (%)4.1%
Missing187
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean13.375983
Minimum1
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:14.962978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median10
Q318
95-th percentile35
Maximum140
Range139
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.306886
Coefficient of variation (CV)0.8453125
Kurtosis13.036072
Mean13.375983
Median Absolute Deviation (MAD)5
Skewness2.3635227
Sum18713
Variance127.84566
MonotonicityNot monotonic
2024-11-09T15:13:15.255606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 125
 
7.9%
3 119
 
7.5%
10 95
 
6.0%
4 91
 
5.7%
7 87
 
5.5%
8 78
 
4.9%
6 75
 
4.7%
15 60
 
3.8%
12 57
 
3.6%
9 49
 
3.1%
Other values (48) 563
35.5%
(Missing) 187
 
11.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 41
 
2.6%
3 119
7.5%
4 91
5.7%
5 125
7.9%
6 75
4.7%
7 87
5.5%
8 78
4.9%
9 49
 
3.1%
10 95
6.0%
ValueCountFrequency (%)
140 1
0.1%
68 1
0.1%
65 1
0.1%
61 1
0.1%
60 1
0.1%
59 1
0.1%
57 2
0.1%
55 2
0.1%
53 2
0.1%
51 2
0.1%

TIME_B_S
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)0.6%
Missing119
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean4.7205181
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:15.693045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8892014
Coefficient of variation (CV)0.61205176
Kurtosis-1.4629003
Mean4.7205181
Median Absolute Deviation (MAD)2
Skewness0.24284572
Sum6925
Variance8.3474846
MonotonicityNot monotonic
2024-11-09T15:13:15.936883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 335
21.1%
9 260
16.4%
1 183
11.5%
3 161
10.2%
6 138
8.7%
7 133
 
8.4%
8 95
 
6.0%
5 82
 
5.2%
4 80
 
5.0%
(Missing) 119
 
7.5%
ValueCountFrequency (%)
1 183
11.5%
2 335
21.1%
3 161
10.2%
4 80
 
5.0%
5 82
 
5.2%
6 138
8.7%
7 133
 
8.4%
8 95
 
6.0%
9 260
16.4%
ValueCountFrequency (%)
9 260
16.4%
8 95
 
6.0%
7 133
 
8.4%
6 138
8.7%
5 82
 
5.2%
4 80
 
5.0%
3 161
10.2%
2 335
21.1%
1 183
11.5%

R_AB_1_n
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing10
Missing (%)0.6%
Memory size12.5 KiB
0.0
1205 
1.0
275 
2.0
 
73
3.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4728
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1205
76.0%
1.0 275
 
17.3%
2.0 73
 
4.6%
3.0 23
 
1.5%
(Missing) 10
 
0.6%

Length

2024-11-09T15:13:16.181154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:16.388949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1205
76.5%
1.0 275
 
17.4%
2.0 73
 
4.6%
3.0 23
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 2781
58.8%
. 1576
33.3%
1 275
 
5.8%
2 73
 
1.5%
3 23
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2781
58.8%
. 1576
33.3%
1 275
 
5.8%
2 73
 
1.5%
3 23
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2781
58.8%
. 1576
33.3%
1 275
 
5.8%
2 73
 
1.5%
3 23
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2781
58.8%
. 1576
33.3%
1 275
 
5.8%
2 73
 
1.5%
3 23
 
0.5%

R_AB_2_n
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing82
Missing (%)5.2%
Memory size12.5 KiB
0.0
1335 
1.0
 
126
2.0
 
42
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4512
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1335
84.2%
1.0 126
 
7.9%
2.0 42
 
2.6%
3.0 1
 
0.1%
(Missing) 82
 
5.2%

Length

2024-11-09T15:13:16.612403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:16.822546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1335
88.8%
1.0 126
 
8.4%
2.0 42
 
2.8%
3.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2839
62.9%
. 1504
33.3%
1 126
 
2.8%
2 42
 
0.9%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2839
62.9%
. 1504
33.3%
1 126
 
2.8%
2 42
 
0.9%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2839
62.9%
. 1504
33.3%
1 126
 
2.8%
2 42
 
0.9%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2839
62.9%
. 1504
33.3%
1 126
 
2.8%
2 42
 
0.9%
3 1
 
< 0.1%

R_AB_3_n
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing98
Missing (%)6.2%
Memory size12.5 KiB
0.0
1392 
1.0
 
80
2.0
 
14
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4464
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1392
87.8%
1.0 80
 
5.0%
2.0 14
 
0.9%
3.0 2
 
0.1%
(Missing) 98
 
6.2%

Length

2024-11-09T15:13:17.058988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:17.265649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1392
93.5%
1.0 80
 
5.4%
2.0 14
 
0.9%
3.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2880
64.5%
. 1488
33.3%
1 80
 
1.8%
2 14
 
0.3%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2880
64.5%
. 1488
33.3%
1 80
 
1.8%
2 14
 
0.3%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2880
64.5%
. 1488
33.3%
1 80
 
1.8%
2 14
 
0.3%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2880
64.5%
. 1488
33.3%
1 80
 
1.8%
2 14
 
0.3%
3 2
 
< 0.1%

NA_KB
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing615
Missing (%)38.8%
Memory size12.5 KiB
1.0
571 
0.0
400 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2913
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 571
36.0%
0.0 400
25.2%
(Missing) 615
38.8%

Length

2024-11-09T15:13:17.484957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:17.655116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 571
58.8%
0.0 400
41.2%

Most occurring characters

ValueCountFrequency (%)
0 1371
47.1%
. 971
33.3%
1 571
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1371
47.1%
. 971
33.3%
1 571
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1371
47.1%
. 971
33.3%
1 571
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1371
47.1%
. 971
33.3%
1 571
19.6%

NOT_NA_KB
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing637
Missing (%)40.2%
Memory size12.5 KiB
1.0
655 
0.0
294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2847
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 655
41.3%
0.0 294
18.5%
(Missing) 637
40.2%

Length

2024-11-09T15:13:17.849082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:18.030182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 655
69.0%
0.0 294
31.0%

Most occurring characters

ValueCountFrequency (%)
0 1243
43.7%
. 949
33.3%
1 655
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1243
43.7%
. 949
33.3%
1 655
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1243
43.7%
. 949
33.3%
1 655
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1243
43.7%
. 949
33.3%
1 655
23.0%

LID_KB
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.2%
Missing629
Missing (%)39.7%
Memory size12.5 KiB
0.0
589 
1.0
368 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2871
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
37.1%
1.0 368
23.2%
(Missing) 629
39.7%

Length

2024-11-09T15:13:18.229235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:18.403072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
61.5%
1.0 368
38.5%

Most occurring characters

ValueCountFrequency (%)
0 1546
53.8%
. 957
33.3%
1 368
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1546
53.8%
. 957
33.3%
1 368
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1546
53.8%
. 957
33.3%
1 368
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1546
53.8%
. 957
33.3%
1 368
 
12.8%

NITR_S
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing8
Missing (%)0.5%
Memory size12.5 KiB
0.0
1404 
1.0
174 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4734
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1404
88.5%
1.0 174
 
11.0%
(Missing) 8
 
0.5%

Length

2024-11-09T15:13:18.611312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:18.814497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1404
89.0%
1.0 174
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 2982
63.0%
. 1578
33.3%
1 174
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2982
63.0%
. 1578
33.3%
1 174
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2982
63.0%
. 1578
33.3%
1 174
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2982
63.0%
. 1578
33.3%
1 174
 
3.7%

NA_R_1_n
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing4
Missing (%)0.3%
Memory size12.5 KiB
0.0
1044 
1.0
383 
2.0
117 
3.0
 
27
4.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4746
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1044
65.8%
1.0 383
 
24.1%
2.0 117
 
7.4%
3.0 27
 
1.7%
4.0 11
 
0.7%
(Missing) 4
 
0.3%

Length

2024-11-09T15:13:19.047683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:19.260999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1044
66.0%
1.0 383
 
24.2%
2.0 117
 
7.4%
3.0 27
 
1.7%
4.0 11
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2626
55.3%
. 1582
33.3%
1 383
 
8.1%
2 117
 
2.5%
3 27
 
0.6%
4 11
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2626
55.3%
. 1582
33.3%
1 383
 
8.1%
2 117
 
2.5%
3 27
 
0.6%
4 11
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2626
55.3%
. 1582
33.3%
1 383
 
8.1%
2 117
 
2.5%
3 27
 
0.6%
4 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2626
55.3%
. 1582
33.3%
1 383
 
8.1%
2 117
 
2.5%
3 27
 
0.6%
4 11
 
0.2%

NA_R_2_n
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing83
Missing (%)5.2%
Memory size12.5 KiB
0.0
1397 
1.0
 
79
2.0
 
26
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4509
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1397
88.1%
1.0 79
 
5.0%
2.0 26
 
1.6%
3.0 1
 
0.1%
(Missing) 83
 
5.2%

Length

2024-11-09T15:13:19.505051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:19.707067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1397
92.9%
1.0 79
 
5.3%
2.0 26
 
1.7%
3.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2900
64.3%
. 1503
33.3%
1 79
 
1.8%
2 26
 
0.6%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2900
64.3%
. 1503
33.3%
1 79
 
1.8%
2 26
 
0.6%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2900
64.3%
. 1503
33.3%
1 79
 
1.8%
2 26
 
0.6%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2900
64.3%
. 1503
33.3%
1 79
 
1.8%
2 26
 
0.6%
3 1
 
< 0.1%

NA_R_3_n
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.2%
Missing102
Missing (%)6.4%
Memory size12.5 KiB
0.0
1414 
1.0
 
57
2.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1414
89.2%
1.0 57
 
3.6%
2.0 13
 
0.8%
(Missing) 102
 
6.4%

Length

2024-11-09T15:13:19.929562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:20.133598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1414
95.3%
1.0 57
 
3.8%
2.0 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2898
65.1%
. 1484
33.3%
1 57
 
1.3%
2 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2898
65.1%
. 1484
33.3%
1 57
 
1.3%
2 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2898
65.1%
. 1484
33.3%
1 57
 
1.3%
2 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2898
65.1%
. 1484
33.3%
1 57
 
1.3%
2 13
 
0.3%

NOT_NA_1_n
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing8
Missing (%)0.5%
Memory size12.5 KiB
0.0
1152 
1.0
351 
2.0
 
51
3.0
 
17
4.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4734
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1152
72.6%
1.0 351
 
22.1%
2.0 51
 
3.2%
3.0 17
 
1.1%
4.0 7
 
0.4%
(Missing) 8
 
0.5%

Length

2024-11-09T15:13:20.343846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:20.549977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1152
73.0%
1.0 351
 
22.2%
2.0 51
 
3.2%
3.0 17
 
1.1%
4.0 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2730
57.7%
. 1578
33.3%
1 351
 
7.4%
2 51
 
1.1%
3 17
 
0.4%
4 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2730
57.7%
. 1578
33.3%
1 351
 
7.4%
2 51
 
1.1%
3 17
 
0.4%
4 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2730
57.7%
. 1578
33.3%
1 351
 
7.4%
2 51
 
1.1%
3 17
 
0.4%
4 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2730
57.7%
. 1578
33.3%
1 351
 
7.4%
2 51
 
1.1%
3 17
 
0.4%
4 7
 
0.1%

NOT_NA_2_n
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing85
Missing (%)5.4%
Memory size12.5 KiB
0.0
1371 
1.0
 
90
2.0
 
37
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4503
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1371
86.4%
1.0 90
 
5.7%
2.0 37
 
2.3%
3.0 3
 
0.2%
(Missing) 85
 
5.4%

Length

2024-11-09T15:13:20.777878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:20.975805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1371
91.3%
1.0 90
 
6.0%
2.0 37
 
2.5%
3.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2872
63.8%
. 1501
33.3%
1 90
 
2.0%
2 37
 
0.8%
3 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2872
63.8%
. 1501
33.3%
1 90
 
2.0%
2 37
 
0.8%
3 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2872
63.8%
. 1501
33.3%
1 90
 
2.0%
2 37
 
0.8%
3 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2872
63.8%
. 1501
33.3%
1 90
 
2.0%
2 37
 
0.8%
3 3
 
0.1%

NOT_NA_3_n
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.2%
Missing102
Missing (%)6.4%
Memory size12.5 KiB
0.0
1394 
1.0
 
53
2.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4452
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1394
87.9%
1.0 53
 
3.3%
2.0 37
 
2.3%
(Missing) 102
 
6.4%

Length

2024-11-09T15:13:21.196851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:21.393800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1394
93.9%
1.0 53
 
3.6%
2.0 37
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 2878
64.6%
. 1484
33.3%
1 53
 
1.2%
2 37
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2878
64.6%
. 1484
33.3%
1 53
 
1.2%
2 37
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2878
64.6%
. 1484
33.3%
1 53
 
1.2%
2 37
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2878
64.6%
. 1484
33.3%
1 53
 
1.2%
2 37
 
0.8%

LID_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing8
Missing (%)0.5%
Memory size12.5 KiB
0.0
1134 
1.0
444 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4734
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1134
71.5%
1.0 444
 
28.0%
(Missing) 8
 
0.5%

Length

2024-11-09T15:13:21.602490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:21.797558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1134
71.9%
1.0 444
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 2712
57.3%
. 1578
33.3%
1 444
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2712
57.3%
. 1578
33.3%
1 444
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2712
57.3%
. 1578
33.3%
1 444
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2712
57.3%
. 1578
33.3%
1 444
 
9.4%

B_BLOK_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing9
Missing (%)0.6%
Memory size12.5 KiB
0.0
1374 
1.0
203 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4731
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1374
86.6%
1.0 203
 
12.8%
(Missing) 9
 
0.6%

Length

2024-11-09T15:13:22.010709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:22.229219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1374
87.1%
1.0 203
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 2951
62.4%
. 1577
33.3%
1 203
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2951
62.4%
. 1577
33.3%
1 203
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2951
62.4%
. 1577
33.3%
1 203
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2951
62.4%
. 1577
33.3%
1 203
 
4.3%

ANT_CA_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing11
Missing (%)0.7%
Memory size12.5 KiB
1.0
1069 
0.0
506 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4725
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1069
67.4%
0.0 506
31.9%
(Missing) 11
 
0.7%

Length

2024-11-09T15:13:22.442908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:22.635802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1069
67.9%
0.0 506
32.1%

Most occurring characters

ValueCountFrequency (%)
0 2081
44.0%
. 1575
33.3%
1 1069
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2081
44.0%
. 1575
33.3%
1 1069
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2081
44.0%
. 1575
33.3%
1 1069
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2081
44.0%
. 1575
33.3%
1 1069
22.6%

GEPAR_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing15
Missing (%)0.9%
Memory size12.5 KiB
1.0
1142 
0.0
429 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4713
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 1142
72.0%
0.0 429
 
27.0%
(Missing) 15
 
0.9%

Length

2024-11-09T15:13:22.847584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:23.043600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1142
72.7%
0.0 429
 
27.3%

Most occurring characters

ValueCountFrequency (%)
0 2000
42.4%
. 1571
33.3%
1 1142
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2000
42.4%
. 1571
33.3%
1 1142
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2000
42.4%
. 1571
33.3%
1 1142
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2000
42.4%
. 1571
33.3%
1 1142
24.2%

ASP_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing15
Missing (%)0.9%
Memory size12.5 KiB
1.0
1190 
0.0
381 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4713
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1190
75.0%
0.0 381
 
24.0%
(Missing) 15
 
0.9%

Length

2024-11-09T15:13:23.280021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:23.486631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1190
75.7%
0.0 381
 
24.3%

Most occurring characters

ValueCountFrequency (%)
0 1952
41.4%
. 1571
33.3%
1 1190
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1952
41.4%
. 1571
33.3%
1 1190
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1952
41.4%
. 1571
33.3%
1 1190
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1952
41.4%
. 1571
33.3%
1 1190
25.2%

TIKL_S_n
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing14
Missing (%)0.9%
Memory size12.5 KiB
0.0
1542 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4716
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1542
97.2%
1.0 30
 
1.9%
(Missing) 14
 
0.9%

Length

2024-11-09T15:13:23.702635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:23.892436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1542
98.1%
1.0 30
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3114
66.0%
. 1572
33.3%
1 30
 
0.6%

TRENT_S_n
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing14
Missing (%)0.9%
Memory size12.5 KiB
0.0
1257 
1.0
315 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4716
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1257
79.3%
1.0 315
 
19.9%
(Missing) 14
 
0.9%

Length

2024-11-09T15:13:24.107793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:24.393790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1257
80.0%
1.0 315
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 2829
60.0%
. 1572
33.3%
1 315
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2829
60.0%
. 1572
33.3%
1 315
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2829
60.0%
. 1572
33.3%
1 315
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2829
60.0%
. 1572
33.3%
1 315
 
6.7%

FIBR_PREDS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1433 
1
153 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

Length

2024-11-09T15:13:24.739651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:25.111522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1433
90.4%
1 153
 
9.6%

PREDS_TAH
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1569 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

Length

2024-11-09T15:13:25.516621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:25.795572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1569
98.9%
1 17
 
1.1%

JELUD_TAH
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1549 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

Length

2024-11-09T15:13:26.182922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:26.575571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1549
97.7%
1 37
 
2.3%

FIBR_JELUD
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1522 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

Length

2024-11-09T15:13:26.963624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:27.323314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1522
96.0%
1 64
 
4.0%

A_V_BLOK
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1536 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Length

2024-11-09T15:13:27.721434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:28.039611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

OTEK_LANC
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1437 
1
149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

Length

2024-11-09T15:13:28.420804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:28.837657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1437
90.6%
1 149
 
9.4%

RAZRIV
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1536 
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Length

2024-11-09T15:13:29.075844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:29.270693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1536
96.8%
1 50
 
3.2%

DRESSLER
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1517 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

Length

2024-11-09T15:13:29.485387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:29.677088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1517
95.6%
1 69
 
4.4%

ZSN
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1213 
1
373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

Length

2024-11-09T15:13:29.880012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:30.069328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1213
76.5%
1 373
 
23.5%

REC_IM
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1441 
1
145 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

Length

2024-11-09T15:13:30.280364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:30.482497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1441
90.9%
1 145
 
9.1%

P_IM_STEN
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
0
1451 
1
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1586
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

Length

2024-11-09T15:13:30.695270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:13:30.895023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1451
91.5%
1 135
 
8.5%

LET_IS
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45208071
Minimum0
Maximum7
Zeros1353
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size12.5 KiB
2024-11-09T15:13:31.068418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.75
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.350513
Coefficient of variation (CV)2.9873271
Kurtosis11.244442
Mean0.45208071
Median Absolute Deviation (MAD)0
Skewness3.4078007
Sum717
Variance1.8238853
MonotonicityNot monotonic
2024-11-09T15:13:31.291425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1353
85.3%
1 87
 
5.5%
3 50
 
3.2%
6 25
 
1.6%
7 22
 
1.4%
4 21
 
1.3%
2 16
 
1.0%
5 12
 
0.8%
ValueCountFrequency (%)
0 1353
85.3%
1 87
 
5.5%
2 16
 
1.0%
3 50
 
3.2%
4 21
 
1.3%
5 12
 
0.8%
6 25
 
1.6%
7 22
 
1.4%
ValueCountFrequency (%)
7 22
 
1.4%
6 25
 
1.6%
5 12
 
0.8%
4 21
 
1.3%
3 50
 
3.2%
2 16
 
1.0%
1 87
 
5.5%
0 1353
85.3%

Interactions

2024-11-09T15:12:07.686820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:10.832463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:15.918111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:19.066009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:22.436721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:26.141735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:30.707331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:33.920376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:37.391793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:41.191404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:45.540757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:49.105047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:52.391952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:55.999524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:00.529791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:04.321786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:07.942373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:11.140975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:16.111587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:19.263152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:22.637866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:26.481146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:30.931923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:34.130125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:37.611671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:41.517279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:45.753204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:49.308501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:52.582555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:56.309792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:00.741404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:04.531654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:16.305414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:19.638740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:22.855285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:27.091305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:31.120317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:34.323845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:12:08.339781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:16.492517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:19.866434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:23.073006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:31.339604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:34.546664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:37.996286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:42.071953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:12.182910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:16.697907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:20.054278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:27.680717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:31.537424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:12:08.740156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:23.469069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:27.985860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:31.722730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:34.976524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:38.395157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:42.689699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:12.835635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:17.079223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:20.428410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:28.346488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:31.919910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:35.167785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:38.613227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:43.008981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:28.862919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:32.298272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:35.583717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:11:43.632633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-11-09T15:12:02.206389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:06.035265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:09.676512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:14.167316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:17.648599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:21.039060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:24.289620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:29.095206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:32.504474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:35.770132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:39.222559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:43.923421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:47.352520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:50.961736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:54.124869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:58.940656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:02.408658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:06.222264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:10.040945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:14.496294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:17.874901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:21.226823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:24.485631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:29.400861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:32.679915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:35.979853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:39.439581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:44.217021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:47.543610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:51.161768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:54.321240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:59.281746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:02.622454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:06.418489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:10.373936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:14.801412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:18.062341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:21.415764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:24.688745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:29.733527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:32.873506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:36.165358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:39.667565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:44.557780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:47.742034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:51.353931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:54.523044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:59.524367image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:02.839840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:06.621011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:10.696374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:15.113155image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:18.247855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:21.597382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:24.874544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:29.933474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:33.069748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:36.633368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:39.949208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:44.739767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:47.944063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:51.529387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:54.804218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:59.714685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:03.041453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:06.840434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:10.997170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:15.292550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:18.429516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:21.776779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:25.106217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:30.101790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:33.241115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:36.815780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:40.241750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:44.941581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:48.139254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:51.705834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:55.125174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:59.899938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:03.230015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:07.036209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:11.352819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:15.494398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:18.627769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:22.012891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:25.444135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:30.306166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:33.492961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:37.004056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:40.599118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:45.135684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:48.345980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:51.935821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:55.348186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:00.100753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:03.432731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:07.249597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:12.075510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:15.715026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:18.866865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:22.216346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:25.800548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:30.517883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:33.698207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:37.203794image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:40.885567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:45.336205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:48.897635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:52.167411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:11:55.677676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:00.310344image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:04.105392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:12:07.468560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-09T15:13:31.747635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AGEALT_BLOODANT_CA_S_nASP_S_nAST_BLOODA_V_BLOKB_BLOK_S_nDLIT_AGDRESSLERD_AD_KBRIGD_AD_ORITFIBR_JELUDFIBR_PREDSFIB_G_POSTFK_STENOKGBGEPAR_S_nGIPER_NAGIPO_KGT_POSTIBS_NASLIBS_POSTIDIM_PG_PINF_ANAMJELUD_TAHKFK_BLOODK_BLOODK_SH_POSTLET_ISLID_KBLID_S_nL_BLOODMP_TP_POSTNA_BLOODNA_KBNA_R_1_nNA_R_2_nNA_R_3_nNITR_SNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nNOT_NA_KBOTEK_LANCO_L_POSTPREDS_TAHP_IM_STENRAZRIVREC_IMROER_AB_1_nR_AB_2_nR_AB_3_nSEXSIM_GIPERTSTENOK_ANSVT_POSTS_AD_KBRIGS_AD_ORITTIKL_S_nTIME_B_STRENT_S_nZSNZSN_Aant_imendocr_01endocr_02endocr_03fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08inf_imlat_imn_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10np_01np_04np_05np_07np_08np_09np_10nr_01nr_02nr_03nr_04nr_07nr_08nr_11post_imritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06
AGE1.000-0.1420.0640.019-0.0900.0610.1720.3030.0410.023-0.0030.0000.1300.0520.1180.1420.1300.0580.0650.0000.0000.1390.2100.0310.0740.0001.000-0.0150.0370.1970.0500.0000.0040.1240.0150.0000.0000.0000.0510.0850.0000.0000.0000.0710.1060.1040.0000.0000.1380.0770.2230.0000.0000.0000.4010.0620.2340.0000.1120.0960.051-0.0360.0000.1360.0630.0000.1640.0550.0730.0000.0520.1680.0000.0720.1720.0000.0110.0270.0000.0760.0000.0000.0420.0000.0000.0280.0250.0000.0850.1150.0290.0430.0000.1280.0610.0360.0000.0000.0000.0000.0640.0000.0410.0790.0000.0710.0000.0000.1000.0000.0000.0580.0250.1310.1310.0180.0000.0000.0100.0560.0740.0550.0440.000
ALT_BLOOD-0.1421.0000.0490.0000.5220.0490.121-0.0870.069-0.043-0.0200.1140.0000.0970.0000.0000.0000.0810.0000.0000.0000.031-0.0960.0690.0000.1521.0000.0330.000-0.0080.1280.036-0.0010.0410.0010.0000.0330.0000.0000.0000.0380.0130.0610.0800.0000.0710.0000.0000.0000.000-0.0250.0000.0000.0000.0560.000-0.0670.000-0.016-0.0640.0000.0270.0000.0000.1360.0000.0000.0670.0000.0580.0000.0000.1500.1080.0000.0000.0340.0470.0000.0000.0001.0000.0000.0000.1030.0000.0000.0430.0460.0000.0000.0000.1340.0810.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0720.1090.0000.0000.000
ANT_CA_S_n0.0640.0491.0000.0820.0420.0520.1380.0770.0000.1170.1860.0000.0290.0240.0000.0640.0150.0410.0000.0000.0000.0000.1490.0000.0000.0541.0000.0000.1270.1600.1020.0000.0710.0180.0550.0710.0070.0100.0270.0380.0390.0000.0200.0000.0070.0390.0000.0000.0000.0230.0000.0000.0000.0000.0340.0000.0600.0000.1710.1960.0000.0650.0000.0340.0000.0570.0290.0000.0000.0600.0200.0360.0000.0000.0000.0000.0000.0230.0000.0730.0000.0000.0640.0130.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0140.0000.0000.0390.0650.0000.0170.0430.0330.0580.0000.0000.000
ASP_S_n0.0190.0000.0821.0000.0050.0480.0140.0490.0000.1350.1930.0000.0000.0000.1110.0800.2450.0000.0000.0000.0000.0000.1750.0000.0300.0001.0000.0000.1650.1900.0000.0440.0550.0300.0000.0230.0420.0690.0630.0000.0000.0370.0000.0440.0000.0000.0000.0000.0410.0000.0000.0000.0350.0000.0130.0260.0780.0000.1240.1760.1080.0710.2360.0000.0670.0000.0000.0000.0000.0000.0000.0260.0000.0160.0000.0000.0460.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0500.0090.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0230.0450.0620.0000.0000.0000.0230.0360.0000.0380.037
AST_BLOOD-0.0900.5220.0420.0051.0000.1170.096-0.0530.000-0.065-0.0600.0000.0830.0000.0000.0250.0000.0000.0340.0000.1970.027-0.0190.0000.0000.0001.0000.0480.0420.0060.0390.0000.0370.041-0.0140.0730.0000.0000.0390.0000.1890.1210.1300.0000.0000.0560.0000.0000.0000.000-0.0280.0000.0000.0000.0590.000-0.0650.000-0.061-0.0860.0000.0760.0000.0540.0420.1030.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.1070.0000.0000.0001.0000.0000.0000.0000.0000.0950.0000.1310.0000.0000.0000.0400.0670.0000.0000.0000.1520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0480.0390.0000.0000.0000.0390.0000.0000.0400.0000.0000.000
A_V_BLOK0.0610.0490.0520.0480.1171.0000.0000.0560.0160.0000.0500.0380.0000.0360.0000.0850.0000.0000.0000.0000.0000.0110.1100.0080.0310.0501.0000.0000.0330.0810.0000.0450.0000.0000.0380.0450.0730.0000.0360.0000.0000.0830.0000.0000.0000.0270.0000.0420.0310.0000.0000.0230.0000.0230.0000.0000.0380.0000.0000.0470.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.2250.1270.0000.0890.0000.0000.2110.0000.0000.0000.0000.0000.0480.0680.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.1400.0000.0130.0000.0000.0430.0000.0000.000
B_BLOK_S_n0.1720.1210.1380.0140.0960.0001.0000.0780.0000.0000.1060.0000.0300.0000.0250.0390.0000.0000.0000.0000.1060.0700.1630.0000.0090.0001.0000.0770.0000.0590.0000.0100.0220.0220.0670.1280.0180.0000.0000.0540.0400.0000.0000.0950.0000.0340.0180.0190.0170.0000.0640.0000.0000.0180.0690.0400.0000.0000.0000.1140.0000.0990.0280.0400.0080.1230.0860.0000.0000.0990.0000.0000.0000.0300.0000.0000.1380.1070.0000.0230.0000.0000.0180.0000.0000.0000.0430.0000.0000.0430.0000.0370.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0120.0000.0000.1160.0100.0000.0000.0000.0000.0000.0320.0230.0000.0000.000
DLIT_AG0.303-0.0870.0770.049-0.0530.0560.0781.0000.0000.2820.2850.0820.0260.0780.0880.5470.0380.0000.0430.0000.0000.1380.0850.0100.0550.0301.0000.0150.0760.1000.0000.0210.0000.0000.0280.0830.0700.0280.0000.0840.0520.0970.0570.0000.0740.1120.0600.0770.1000.0540.1650.0280.0320.0000.3600.1460.2100.1210.3210.3500.0530.0000.0000.0000.0520.0510.2020.0950.0710.0000.0420.0840.0360.0930.0000.0190.0000.0630.0000.0000.1700.0200.0000.0000.0000.0000.0470.0000.0000.0460.0000.0330.0410.0030.0240.1520.0000.0000.0750.0000.0430.0000.0000.0000.0000.0270.0720.0000.0000.0000.0000.0000.0250.1260.0500.0000.0000.0980.0480.0000.0650.0000.0000.000
DRESSLER0.0410.0690.0000.0000.0000.0160.0000.0001.0000.0000.0000.0000.0000.0000.0480.0620.0090.0090.0000.0000.0000.0680.2630.0000.0610.0001.0000.0000.0000.0000.0000.0450.0230.0000.1600.0460.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0180.0000.0630.0000.0110.0340.0000.0000.0200.0550.0270.0560.0580.0000.0200.0000.0010.1200.0000.0000.0000.0000.0500.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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OTEK_LANC0.1060.0000.0070.0000.0000.0000.0000.0740.0000.0000.0000.0000.0170.0000.1360.1190.0000.0000.0620.0210.1700.0620.2960.0240.0970.0001.0000.0440.0000.2270.0000.0330.0430.0370.0460.0930.1870.1790.1480.3390.0000.0000.0000.0581.0000.1330.0000.0670.0310.1700.0870.1050.1130.0850.0570.0000.1220.0000.0460.0000.0000.0750.0000.1170.0760.0450.0920.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0580.0000.0000.0000.0000.0000.0100.0000.0000.0010.0000.0160.0000.0000.0000.0000.0180.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0420.0000.0000.0000.0230.1290.0660.0000.0000.1150.0170.0000.0770.0000.0000.000
O_L_POST0.1040.0710.0390.0000.0560.0270.0340.1120.0000.0910.1360.0000.0000.0000.0910.0720.0330.0000.0690.0000.0000.0000.1140.0220.1170.0001.0000.1080.0900.1580.0000.0000.0900.0440.0670.0330.0440.1360.0700.2780.0150.0370.0000.0000.1331.0000.0330.0460.0000.0000.0680.0000.1150.0740.0700.0000.0770.1120.0250.1100.0000.0770.0000.0000.1430.0380.0450.0000.0450.0000.0000.0190.0000.0000.0000.0000.0000.0550.0000.0000.0000.0410.0000.0000.0830.0620.0180.0000.0000.0000.0000.0000.0000.0470.0000.0810.0000.0000.0000.0000.0110.0000.0000.0000.0000.1180.0000.0410.0000.0000.0000.0000.0210.1540.0000.0310.0000.1700.0220.0000.0810.0150.0000.000
PREDS_TAH0.0000.0000.0000.0000.0000.0000.0180.0600.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0230.0001.0000.0000.0390.0000.0000.0001.0000.0320.0000.0000.0000.0000.0210.0000.0220.0000.0870.0000.0000.0200.0000.0320.0740.0000.0000.0331.0000.0000.0000.0000.0000.0490.0000.0000.0000.0180.0000.2140.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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ROE0.223-0.0250.0000.000-0.0280.0000.0640.1650.0000.0570.0480.0000.0630.1010.0550.0340.0380.0000.0000.0000.0000.0620.0700.0450.0490.1191.000-0.0170.0760.0810.0960.000-0.0190.000-0.0330.0960.0000.0280.0530.0580.0000.0000.0940.0000.0870.0680.0000.0000.0000.0191.0000.0000.0410.0750.2030.0080.1090.0000.0540.0700.0000.0980.0000.0810.0000.0000.1320.0000.1040.0000.0000.0780.0000.0000.0000.0000.0200.0000.0000.0000.0001.0000.0000.0000.1340.0000.0000.0000.0000.0130.0000.0000.0000.0810.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0330.0000.0000.1040.0000.0320.0000.0000.0300.000
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SEX0.4010.0560.0340.0130.0590.0000.0690.3600.0180.1250.0600.0080.0910.0000.1000.2630.0000.0000.0490.0000.0000.1020.1220.0000.0540.0091.0000.0180.0000.1350.0000.1060.0000.0570.0660.0000.0000.0000.0570.0530.0710.0500.0490.0000.0570.0700.0000.0000.0720.0650.2030.0550.0000.0721.0000.0580.0740.0000.1960.1420.0000.0460.0600.0970.1070.0000.2680.1010.0950.0410.0190.1040.0000.0090.0190.0000.0470.0300.0000.0000.0000.0000.0000.0090.0000.0000.0500.0000.0000.0000.0000.0200.0170.0870.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0470.0000.0190.0080.0000.0000.0000.0000.1000.0560.0260.0000.0990.0170.0110.0000.0000.0290.000
SIM_GIPERT0.0620.0000.0000.0260.0000.0000.0400.1460.0000.0670.1010.0000.0000.0000.0000.2320.0280.0000.0070.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0140.0000.0000.0470.0070.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0180.0000.0250.0000.0080.0000.0000.0000.0581.0000.0000.0000.0000.1530.0000.0140.0000.0000.0000.0410.0680.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0070.0000.0000.0000.0000.0000.000
STENOK_AN0.234-0.0670.0600.078-0.0650.0380.0000.2100.063-0.039-0.0120.0000.0610.0190.5050.0620.0000.0860.0130.0000.2680.5310.1890.0000.2380.0001.0000.0070.0630.1580.0000.067-0.0170.020-0.0050.0860.0410.0660.0990.1040.0280.0490.0000.0000.1220.0770.0000.1080.0610.1280.1090.0590.0550.0820.0740.0001.0000.014-0.0080.0180.019-0.0240.0650.0440.0900.0630.1080.0260.0000.0000.0000.0910.0140.0420.0000.0660.0330.0220.0000.0950.0520.0630.0000.0000.0490.0210.0000.0000.0000.0000.0000.0580.0000.0320.0810.0170.0000.0510.0490.0530.0040.0000.0000.0000.0590.0530.0000.0360.0790.0000.0630.0830.0000.0790.0700.0480.0000.0560.0030.0330.0900.0000.0080.000
SVT_POST0.0000.0000.0000.0000.0000.0000.0000.1210.0000.0000.0000.0000.0460.0000.0000.0000.0080.0000.0000.0001.0000.0000.0660.0000.0000.0001.0000.0000.0000.1290.0000.0240.0000.0000.0200.0000.0000.0470.0000.0380.0000.0000.0070.0340.0000.1120.2140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0141.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.5440.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1280.0000.0000.0000.0340.0000.0000.0000.0000.0270.0000.0000.000
S_AD_KBRIG0.112-0.0160.1710.124-0.0610.0000.0000.3210.0110.8570.4860.0000.0000.0000.0340.1320.0000.0000.0000.2180.0000.000-0.1650.1120.0430.0001.000-0.0130.510-0.1400.0000.000-0.1430.000-0.0190.0000.0000.0260.0830.0000.0910.1540.1260.1090.0460.0250.0000.0780.0000.0000.0540.0580.0000.0000.1960.000-0.0080.0001.0000.5520.0000.1070.1050.0000.1940.0970.0850.0700.0000.0000.0650.0721.0000.0000.0000.0000.0770.0510.0000.0800.0000.3530.0000.0000.0000.0000.1070.0000.1800.1460.0000.0000.0880.1680.0860.0000.0001.0000.0001.0000.1281.0000.0001.0000.0000.0000.2580.0000.1060.1620.3290.1650.0000.1650.1460.0001.0000.1620.0000.1350.1590.0290.1690.000
S_AD_ORIT0.096-0.0640.1960.176-0.0860.0470.1140.3500.0340.4680.8360.0860.0000.1180.0000.1860.1110.0000.0000.0940.3810.035-0.1470.0000.0720.000NaN0.0080.712-0.1500.0000.058-0.1110.0840.0620.0000.0200.0270.0340.0550.0000.0000.0250.0690.0000.1100.0000.0880.0000.0000.0700.0000.0000.0060.1420.1530.0180.0000.5521.0000.0000.1070.0000.0630.1120.0350.0900.0360.0000.0000.1280.0000.0000.0000.0000.0000.0630.0001.0000.0930.0670.1880.1050.0000.0000.0000.0000.0000.0900.0000.0000.0000.0590.0000.0850.0000.0000.0000.0000.0000.0000.0000.0911.0000.1240.0000.0000.0090.0670.0000.0000.0000.0000.1190.0890.0781.0000.0500.0000.0000.1200.0520.0460.000
TIKL_S_n0.0510.0000.0000.1080.0000.0000.0000.0530.0000.0000.0000.0180.0000.0000.0000.0420.0000.0000.0260.0000.0000.0000.0780.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.1360.0000.0260.1770.0290.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.1800.1240.0000.0000.0190.0000.0000.0001.0000.0260.0460.0140.0000.0000.0250.0000.0000.0000.0000.0990.0000.0000.0220.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0100.0000.0000.0000.0220.0000.0250.0000.0000.0000.000
TIME_B_S-0.0360.0270.0650.0710.0760.0000.0990.0000.0000.0690.1150.0710.0000.0250.0000.0000.1740.0000.1130.0000.0000.044-0.1160.0540.0400.0891.0000.1060.051-0.1500.1140.166-0.1260.0000.0760.2870.1060.0510.0550.0810.0430.0610.0340.1330.0750.0770.0000.0430.1100.0930.0980.0640.0220.0400.0460.014-0.0240.0000.1070.1070.0261.0000.0000.0480.0350.0700.0550.0620.0640.0600.0800.1250.0000.0310.0000.0000.0580.0310.0000.0650.0000.0000.0000.0230.0000.0000.0160.0350.0000.0000.0000.0460.0000.0000.0000.0000.0190.0000.0000.1350.0420.0000.0000.0080.0420.0450.0580.0200.0000.0080.0550.0000.0000.0000.0000.0000.0000.0180.0180.0000.0700.0000.0000.000
TRENT_S_n0.0000.0000.0000.2360.0000.0000.0280.0000.0200.0000.0000.0000.0000.0000.0000.0100.0220.0060.0000.0000.0000.0510.2230.0000.0000.0001.0000.0730.0000.0000.0590.0000.0000.0000.0210.0650.0490.0000.0000.0290.0590.0330.0000.0000.0000.0000.0000.0230.0000.0240.0000.0000.0000.0000.0600.0000.0650.0000.1050.0000.0460.0001.0000.0260.0100.0000.0570.0000.0000.0000.0000.0200.0000.0350.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0170.0000.0340.0450.0000.0000.0700.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0610.0000.0240.0000.025
ZSN0.1360.0000.0340.0000.0540.0000.0400.0000.0550.0000.0320.0000.1000.0000.0000.0740.0350.0000.0000.0080.0650.0000.0990.0000.0000.0001.0000.0000.0550.2400.0350.0130.0430.0740.0000.0320.0280.0910.1070.0240.1150.0350.0380.0610.1170.0000.0000.0290.0560.0860.0810.0640.0400.0750.0970.0000.0440.0000.0000.0630.0140.0480.0261.0000.3920.0830.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0180.0000.0350.0770.0000.0000.0000.0000.0000.0000.0000.0000.0350.0100.0390.0000.0140.0660.0000.0000.0000.0000.0670.0880.0000.0000.0280.0000.1060.0000.0000.0000.000
ZSN_A0.0630.1360.0000.0670.0420.0000.0080.0520.0270.1730.1430.0420.0000.0400.1030.0190.0810.0000.0000.0000.0000.0810.2090.0000.1050.0001.0000.0000.1360.1630.0520.0500.0510.1870.0000.0390.0180.0180.0220.0570.0360.0390.0310.0610.0760.1430.0000.0000.0480.0000.0000.0000.0000.0000.1070.0000.0900.0490.1940.1120.0000.0350.0100.3921.0000.0350.0750.0710.0050.0000.0000.0510.0000.0000.0000.0000.0000.0560.0000.0780.0000.0000.0470.0000.0710.0000.0590.0000.0910.0000.0000.0000.0970.0810.2010.1040.0000.0000.1260.0000.0480.1880.0000.0000.1680.2340.1020.0750.2430.0000.0000.0320.0000.1850.1580.0000.0840.1250.0000.0160.0810.1120.0000.016
ant_im0.0000.0000.0570.0000.1030.1310.1230.0510.0560.1610.0280.0800.0660.0400.0570.0580.0530.0000.0220.0000.0000.0760.0980.0740.0740.0821.0000.0290.0240.1000.1190.1970.0750.0000.0490.2270.0940.0450.0450.0730.0520.0330.0530.0000.0450.0380.0000.1060.1220.0520.0000.0480.0190.0000.0000.0410.0630.0000.0970.0350.0000.0700.0000.0830.0351.0000.0220.0000.0000.0000.0830.0580.0440.0000.0000.0000.4110.3680.0000.0000.0811.0000.0720.1430.0190.0350.0530.0370.1150.0560.0320.0930.1070.0000.0000.0000.0000.0000.0000.0000.0530.0000.0410.0000.0000.0680.0000.0000.0001.0000.0000.0610.2270.0550.0000.0530.0000.1320.0520.0000.0420.0370.0000.081
endocr_010.1640.0000.0290.0000.0000.0000.0860.2020.0580.0000.0370.0180.0000.0000.1320.1150.0000.0000.0000.0000.0000.1190.0820.0180.1020.0001.0000.0690.0250.1220.0000.0590.0680.0000.0000.0000.0520.0400.0990.0760.0000.0000.0000.0000.0920.0450.0500.0000.0000.0580.1320.0220.0450.0450.2680.0680.1080.0000.0850.0900.0250.0550.0570.1200.0750.0221.0000.0680.0070.0000.0000.0730.0000.0000.0000.0000.0380.0000.0070.0000.0000.0000.0290.0000.0000.0000.0080.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0060.0850.0300.0420.0000.0000.0000.000
endocr_020.0550.0670.0000.0000.0000.0000.0000.0950.0000.0000.0000.0000.0000.0000.0250.0330.0000.0000.0000.0000.0000.0090.0690.0000.0000.0001.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0880.0000.0000.0330.0090.0000.0000.0000.0000.0000.0000.0080.0720.0000.0000.0540.0000.0000.1010.0660.0260.0000.0700.0360.0000.0620.0000.0000.0710.0000.0681.0000.0000.0000.0000.0000.0000.0000.0100.0000.0180.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0080.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0310.0000.0000.0520.0080.0000.0770.0100.0000.0000.0000.0000.0000.000
endocr_030.0730.0000.0000.0000.0000.0000.0000.0710.0200.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0570.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.1040.0000.0000.0000.0950.0000.0000.0000.0000.0000.0000.0640.0000.0000.0050.0000.0070.0001.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.000
fibr_ter_010.0000.0580.0600.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0001.0000.0000.1040.0000.0360.0001.0000.0000.0000.0000.0000.0370.1210.0000.0690.0000.0550.0000.0000.0000.0580.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_020.0520.0000.0200.0000.0000.0260.0000.0420.0010.0000.1880.0100.0000.0000.0330.0350.0490.0000.0000.0001.0000.0000.0560.0340.0000.0001.0000.0000.0510.0420.0000.0660.0530.0000.0000.0270.1000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0040.0000.0000.0000.0590.0000.0000.0190.0000.0000.0000.0650.1280.0000.0800.0000.0000.0000.0830.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_030.1680.0000.0360.0260.0000.0000.0000.0840.1200.0000.0390.0180.0000.0000.0790.1100.0730.0560.0640.0000.0000.1150.1540.0000.0640.0831.0000.0460.0750.0000.0490.1080.0420.0400.1290.0590.1040.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0780.0510.0000.0000.1040.0000.0910.0000.0720.0000.0990.1250.0200.0000.0510.0580.0730.0000.0000.0000.0001.0000.0000.0000.0000.0000.0560.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.1100.0000.0360.0000.0000.0000.0520.0190.0000.0000.0000.000
fibr_ter_050.0000.1500.0000.0000.0780.0000.0000.0360.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0140.0520.0000.0000.0001.0000.0180.0000.0001.0000.0470.1070.0000.0001.0000.1710.0000.0000.0000.0580.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0001.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.000
fibr_ter_060.0720.1080.0000.0160.0000.0000.0300.0930.0000.0000.0000.0470.0000.0350.0430.0000.0220.0000.0000.0000.0000.0000.1050.0000.0000.0001.0000.0000.0000.0000.0090.0370.0000.0000.0000.0270.1660.3560.0850.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0650.3530.2450.0090.0000.0420.0000.0000.0000.0000.0310.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_070.1720.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0150.0050.0000.0151.0000.0000.0000.0000.0000.0110.1020.0000.0000.0000.0000.0000.0320.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0190.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_080.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0170.0000.0100.0001.0000.0000.0000.0821.0000.0000.0000.0000.0001.0000.1920.0001.0000.0000.0000.0001.0001.0000.0250.0000.0000.0000.0000.0000.0000.0340.0001.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
inf_im0.0110.0340.0000.0460.0000.2250.1380.0000.0500.0700.0460.0000.0360.0540.0200.0430.0280.0000.0000.0330.1340.0680.0640.1130.0190.0361.0000.0000.0650.0580.0790.0630.0000.0680.0350.0920.0470.0550.0000.0000.0000.0430.0000.0000.0000.0000.0000.0540.0650.0000.0200.0140.0300.0000.0470.0000.0330.0720.0770.0630.0530.0580.0000.0000.0000.4110.0380.0180.0410.0000.0000.0560.0000.0000.0000.0001.0000.2970.0000.0860.0001.0000.1300.1010.0000.0000.0580.0000.0370.1210.0150.0000.0000.0370.0000.0560.0850.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0471.0000.0000.0000.1970.0000.0890.1050.0530.0870.0900.0610.0000.0000.0000.032
lat_im0.0270.0470.0230.0000.1070.1270.1070.0630.0370.0630.0330.0310.0460.0330.0160.0500.0440.0350.0000.0000.0000.0430.0690.0630.0490.0561.0000.0080.0720.0840.0550.0880.0860.0000.0140.0900.0520.0000.0000.0370.0340.0510.0650.0200.0580.0550.0360.0470.1150.0400.0000.0760.0000.0000.0300.0130.0220.0000.0510.0000.0000.0310.0760.1130.0560.3680.0000.0000.0000.0000.0190.0530.1290.0400.0000.0000.2971.0000.0000.0370.0001.0000.0710.1210.0000.0000.0390.0000.0880.0540.0370.0350.0440.0520.0000.0000.0000.0000.0470.0000.0230.0000.0000.0000.0000.0000.0620.0300.0001.0000.0370.0000.1340.0500.0270.0180.0000.1390.0510.0000.0000.0000.0000.000
n_p_ecg_p_010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0230.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0070.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_p_ecg_p_030.0760.0000.0730.0000.0000.0890.0230.0000.0000.1760.1820.0740.0000.1150.0000.0520.0000.0000.0000.0000.0000.0000.0800.0000.0400.0821.0000.0000.0000.1010.0000.0000.0120.0000.0690.0380.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0220.0590.0000.0000.0000.0000.0000.0000.0000.0950.0890.0800.0930.0000.0650.0000.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0370.0001.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0380.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0450.0000.0000.0060.0000.0000.0300.0000.0000.0000.0000.0000.000
n_p_ecg_p_040.0000.0000.0000.0000.0000.0000.0000.1700.0000.1030.0000.0000.0000.0000.0270.1240.0000.0000.0000.0001.0000.0000.0200.0000.0270.0001.0000.0000.0300.0520.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0670.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0700.000
n_p_ecg_p_050.0001.0000.0000.0001.0000.0000.0000.0200.0000.4270.1430.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.0000.0210.0000.0000.0001.0001.0000.0860.0790.0000.0001.0000.0001.0000.0000.0001.0001.0000.0200.0001.0001.0000.0000.0000.0410.0000.0000.0000.0001.0000.0001.0001.0000.0000.0000.0630.0000.3530.1880.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_p_ecg_p_060.0420.0000.0640.0610.0000.2110.0180.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0440.0640.0740.0160.0381.0000.1350.0000.0550.0000.0210.1470.0000.0000.0000.0000.0610.0000.0000.0320.0760.0690.0000.0000.0000.0000.0000.0200.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.1050.0000.0000.0000.0000.0470.0720.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.1300.0710.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.6130.1160.0440.0000.0090.0600.0000.0000.000
n_p_ecg_p_070.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0170.0660.0000.0000.0150.0000.0000.0380.0000.0240.0600.0181.0000.0000.0000.0000.0000.0250.0930.0130.0340.0660.0000.0000.0000.0000.0000.0360.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1010.1210.0000.0120.0000.0000.0001.0000.0000.0000.0140.0000.0470.0000.0330.0000.0390.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0000.0190.0000.0380.0000.0000.0000.0410.0000.0000.0000.0610.0230.0000.0000.0090.0000.000
n_p_ecg_p_080.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0990.0000.0000.0000.0050.0001.0000.0360.0000.0000.0000.0001.0000.1330.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0830.0000.0000.0000.0000.1340.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0710.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0000.0000.0520.0390.0000.0000.0000.0370.0000.0000.0000.0000.0000.025
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n_p_ecg_p_120.0850.0460.0000.0690.1310.0480.0000.0000.0000.0910.0410.0500.0080.0000.0360.0000.0000.0000.0000.0000.0440.0000.1320.0600.0590.0001.0000.0000.0630.2090.0000.0000.0580.0000.0000.0000.0720.0000.0000.0000.0150.0300.0880.0000.0160.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.1800.0900.0000.0000.0450.0000.0910.1150.0000.0000.0000.0000.0000.0000.0000.0460.0000.0470.0370.0880.0000.0000.0000.0000.0000.0470.0000.0000.0000.0001.0000.0000.0000.0000.0460.0000.0000.0000.0000.0220.0000.0210.0000.0000.0000.1150.0000.0000.0000.0000.0160.0000.0000.0000.0120.0180.0340.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_010.1150.0000.0000.0000.0000.0680.0430.0460.0000.1210.0490.0000.0950.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0320.0280.0001.0000.1250.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0560.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.1210.0540.0570.0500.0000.0000.0000.0000.0000.0400.0000.0000.0001.0000.0000.0680.0210.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0150.0150.0000.0000.0000.0000.0150.0000.0820.0000.0010.0000.0000.0020.0000.0470.0000.0000.0000.000
n_r_ecg_p_020.0290.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0240.0000.0000.0001.0000.0000.0000.0340.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0050.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0370.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_030.0430.0000.0000.0000.0000.0000.0370.0330.0000.0000.0000.0270.0000.0000.0660.0000.0000.0000.0000.0000.0000.0650.0380.0000.0000.0561.0000.0000.0000.0000.0000.2000.0490.0680.0000.0500.0940.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0360.0000.0000.0000.0410.0000.0000.0200.0000.0580.0000.0000.0000.0000.0460.0700.0180.0000.0930.0000.0000.0000.0990.0070.0440.0000.0380.0130.0110.0000.0350.0000.0000.0120.0000.0000.0000.0000.0000.0170.0140.0000.0680.0001.0000.0730.0740.0390.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0350.0110.0000.0000.0000.0000.0690.0000.0120.0000.0000.0880.0000.0000.0000.000
n_r_ecg_p_040.0000.1340.0000.0000.0400.0090.0230.0410.0000.2040.0000.0220.0000.0260.0460.0290.0340.0000.0000.0000.0000.0000.0840.0000.0370.0591.0000.0000.0000.0660.0680.1870.0000.0220.0490.0580.0380.0000.0000.0120.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0170.0120.0000.0000.0880.0590.0150.0000.0000.0000.0970.1070.0190.0000.0220.0000.0530.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0390.0000.0000.0420.0000.0460.0210.0000.0731.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0350.0000.0000.0010.0000.0000.0600.0000.0000.000
n_r_ecg_p_050.1280.0810.0000.0000.0670.0000.0000.0030.0000.0540.0880.0000.1980.0690.0000.0250.0000.0000.0000.0690.0000.0000.1360.0230.0550.0001.0000.0000.0000.1490.0000.0000.0000.5810.0000.0000.0270.0000.0670.0550.0330.0000.0410.0410.0180.0470.0000.0000.0000.0000.0810.0320.0220.0000.0870.0000.0320.0000.1680.0000.0000.0000.0000.0350.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0520.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0220.0000.0740.0261.0000.0000.0000.0000.0000.0270.0000.0350.0000.0000.0000.0000.0000.0040.1780.1120.0000.0000.1000.0170.2530.6350.0000.0000.0480.0000.0000.0250.1140.0000.000
n_r_ecg_p_060.0610.0680.0000.0500.0000.0000.0000.0240.0000.0000.0320.0240.0000.0000.0840.0000.0470.0000.0180.0231.0000.0690.0680.0000.0290.0001.0000.0000.0000.1160.0000.0000.0000.4740.0360.0000.0150.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0220.0440.0000.0000.0000.0000.0000.0810.0000.0860.0850.0000.0000.0410.0770.2010.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0280.0000.0000.0000.0390.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.6670.0000.0000.0000.0000.1960.5330.0000.0000.0650.0000.0000.0000.0000.0000.000
n_r_ecg_p_080.0360.0000.0000.0090.0000.0000.0000.1520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0630.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.1480.0000.0000.0350.0000.0000.0000.0000.0000.0300.0000.0810.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.5440.0000.0000.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1330.0000.0210.0000.0810.0000.0000.0000.0000.0000.0370.0000.000
n_r_ecg_p_090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1541.0000.0000.0000.0000.0000.0471.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_100.0000.0000.0000.0000.1520.0000.0000.0000.0001.0000.0000.1310.0000.0000.0910.0240.0000.0000.0000.0001.0000.0000.0000.0430.0000.0001.0000.0000.0000.0000.0000.0270.0000.0110.0650.0000.0460.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0510.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.000
np_010.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0430.0001.0000.0000.0000.0680.0000.0000.0000.0100.0000.0000.0000.1280.0000.0000.0000.0520.0000.0000.0000.0000.0000.0000.0370.0780.0000.0000.1120.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.1260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_040.0000.0000.0210.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0370.0000.0000.0460.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0001.0000.0000.0000.1350.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_050.0640.0000.0000.0000.0000.0000.0000.0430.0000.1830.0730.0000.0000.0230.0000.0000.0000.0000.0000.0001.0000.0000.0720.0000.0600.0001.0000.0000.0000.0000.0000.0000.0200.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0400.1280.0000.0670.0420.0000.0000.0480.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0930.0000.0000.0680.0000.0000.0000.0480.0000.0000.0350.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0050.0000.0000.1470.0000.0000.0000.0190.0580.0000.0000.0000.0000.0000.0000.0000.0000.000
np_070.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.2970.0000.0000.0000.0001.0000.0000.0000.0000.0100.0001.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
np_080.0410.0000.0000.0030.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0150.0000.0000.0000.0001.0000.0000.0610.0000.0000.0001.0000.0000.0170.0830.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0120.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_090.0790.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.2060.0000.0000.0000.0000.0001.0000.0000.0740.0430.1070.0001.0000.0930.0000.0000.0000.0000.0370.0000.0000.0000.1230.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0950.0000.0000.0000.0000.0001.0001.0000.0000.0080.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1150.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0610.0000.0000.0000.0000.0001.0000.0000.0620.0000.0070.0001.0000.0930.0420.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.0000.0000.0000.0000.0590.0000.0000.1240.0000.0420.0000.0100.1680.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0450.0000.0000.0000.0000.0420.0000.0000.0000.0140.0000.0000.0000.0000.0000.000
nr_010.0710.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0400.0000.1240.0000.0000.0000.0000.0000.0000.0000.0380.0000.0170.0001.0000.2290.0000.1020.0000.0000.0000.0000.0000.0000.0000.0810.0440.0000.0000.0000.0000.0000.0410.1180.0000.0000.0000.0000.0000.0000.0640.0050.0470.0000.0530.0000.0000.0000.0000.0450.0470.0390.2340.0680.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0390.0000.0000.0000.0000.0000.000
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nr_030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1520.0000.0590.0000.0000.0000.0000.0090.0000.0000.0780.0000.0490.0001.0000.0000.0000.0730.0140.0000.0000.1520.0000.0000.0120.0000.0710.0490.0360.0490.0000.0000.0000.0410.0000.0000.0000.0470.0000.0580.0000.0000.0190.0000.0360.0000.0000.0090.0000.0200.0000.0140.0750.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0190.0000.0000.0130.0000.0000.0000.0000.0000.0000.1780.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0001.0000.0000.0000.0000.0000.0200.0800.1280.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_040.1000.0000.0210.0240.0000.0000.0140.0000.0000.0430.0970.0240.0000.0000.0590.0190.0000.0000.0000.0191.0000.0500.1040.0000.0300.0001.0000.0000.0000.1680.0000.0000.0500.4690.0000.0000.0380.0000.0000.0000.0330.0000.0000.0000.0420.0000.0000.0190.0000.0000.0000.0100.0000.0000.0080.0000.0790.0000.1060.0670.0000.0000.0000.0660.2430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0160.0000.0000.0350.0000.1120.6670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0001.0000.0000.0000.0000.0000.1840.5030.0000.0000.0460.0000.0000.0000.0200.0000.000
nr_070.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0001.0000.0230.0190.0000.1060.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1620.0000.0000.0080.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_080.0000.0000.0000.0000.0000.0000.0000.0000.0000.2440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0001.0000.2770.0000.0000.0000.0000.2780.0000.0000.0000.0000.0340.0590.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0630.0000.3290.0000.0000.0550.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.000
nr_110.0580.0000.0140.0000.0660.0000.0000.0000.0240.2440.0000.0000.1010.0000.0970.0000.0000.0000.0000.0000.0000.0680.1050.0000.0310.0001.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0410.0320.0000.0000.0000.0000.0000.0000.0000.0000.0330.0110.0000.0000.0000.0000.0000.0000.0000.0000.0830.1280.1650.0000.0000.0000.0000.0000.0320.0610.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0300.1000.0000.1330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0400.0560.0000.0000.0000.0260.0000.0000.0000.0000.000
post_im0.0250.0000.0000.0000.0480.0310.1160.0250.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.0630.0270.0970.0240.0640.0090.0220.0001.0000.0290.0350.0000.0000.0540.0000.0000.0000.1510.0570.1050.0470.0000.0000.0680.0000.0940.0230.0210.0000.0200.0000.0000.0000.0000.0780.0540.0000.0000.0000.0000.0000.0000.0040.0000.0650.0000.0000.2270.0000.0000.0120.0000.0000.1100.0590.0000.0000.0000.1970.1340.0600.0000.0001.0000.0000.0000.0520.0000.0000.1810.0120.0820.0000.0000.0000.0170.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0001.0000.0670.0001.0000.0000.0000.0000.0000.0610.0670.0000.0000.0000.0760.000
ritm_ecg_p_010.1310.0000.0000.0230.0390.0000.0100.1260.0250.1660.1340.0000.0540.0330.1050.0770.0000.0000.0270.0000.0000.0200.1660.0420.0480.0001.0000.0000.1250.1860.0500.0000.1090.3330.0780.0000.0000.0580.0000.1290.0270.0000.0000.0120.1290.1540.0000.0000.0000.0000.0990.0000.0250.0000.1000.0120.0790.0000.1650.1190.0100.0000.0000.0670.1850.0550.0730.0520.0000.0000.0000.0000.0000.0000.0110.0000.0000.0500.0000.0000.0110.0000.1200.0410.0390.0000.1190.0000.0180.0000.0000.0000.0000.2530.1960.0210.0000.0000.0000.0000.0191.0000.0000.0000.0420.0210.0000.0800.1840.0000.0000.0400.0001.0000.3560.1480.0000.7760.2400.0000.0490.0550.0000.034
ritm_ecg_p_020.1310.0360.0390.0450.0000.0000.0000.0500.0000.1140.1000.0190.1020.0000.0760.0260.0430.0000.0000.0000.0000.0580.1050.0510.0000.0001.0000.0000.0270.1900.0000.0000.0270.8330.0230.0000.0530.0000.0000.0640.0000.0000.0000.0220.0660.0000.0000.0000.0000.0320.0330.0000.0350.0000.0560.0000.0700.0000.1460.0890.0000.0000.0000.0880.1580.0000.0000.0080.0000.0000.0000.0360.0000.0000.0000.0000.0890.0270.0000.0060.0000.0000.0000.0000.0000.0000.0500.0000.0340.0010.0000.0690.0350.6350.5330.0000.0000.0420.0000.0000.0581.0000.0000.0000.0000.0000.0000.1280.5030.0000.0000.0560.0000.3561.0000.0000.0000.1290.0210.0000.0050.0880.0000.000
ritm_ecg_p_040.0180.0000.0650.0620.0000.1400.0000.0000.0000.0000.1120.0000.0000.0000.0200.0000.0000.0000.0000.0001.0000.0000.0730.0300.0000.0361.0000.1400.0460.0530.0000.0000.0220.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0180.0000.0000.0000.0000.0000.0260.0000.0480.0340.0000.0780.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1050.0180.0000.0000.0000.0000.6130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1480.0001.0000.0000.0440.0000.0100.0590.0410.0000.000
ritm_ecg_p_060.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.0060.0000.0710.0000.0001.0001.0000.0000.0001.0000.0000.3220.0001.0001.0000.0000.0000.0000.0200.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0840.0000.0060.0770.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
ritm_ecg_p_070.0000.0000.0170.0000.0390.0130.0000.0980.0000.1790.0440.0000.0000.0200.0870.0720.0000.0000.0560.0000.0000.0810.1210.0000.0690.0001.0000.0000.1030.1580.0450.0000.0650.0590.0750.0000.0000.0470.0450.1170.0440.0490.0160.0000.1150.1700.0000.0000.0000.0510.1040.0000.0250.0410.0990.0070.0560.0000.1620.0500.0220.0180.0000.0280.1250.1320.0850.0100.0080.0000.0000.0000.0000.0000.0000.0000.0870.1390.0000.0300.0000.0000.0440.0610.0370.0000.0730.0000.0000.0020.0000.0000.0010.0480.0650.0000.0000.0000.0000.0000.0001.0000.0000.0000.0140.0390.0000.0000.0460.0000.0000.0000.0610.7760.1290.0440.0001.0000.0830.0000.0100.0000.0000.038
ritm_ecg_p_080.0100.0000.0430.0000.0000.0000.0000.0480.0000.0000.0000.0000.0440.0000.0190.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0001.0000.0330.0000.0000.0010.0000.0000.0250.0000.0000.0270.0150.0000.0300.0000.0000.0000.0210.0170.0220.0000.0080.0000.0000.0000.0000.0400.0000.0170.0000.0030.0000.0000.0000.0000.0180.0000.0000.0000.0520.0300.0000.0000.0000.0000.0520.0270.0000.0000.0000.0900.0510.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0670.2400.0210.0000.0000.0831.0000.0250.0000.0000.0000.000
zab_leg_010.0560.0720.0330.0230.0000.0000.0320.0000.0000.0600.0000.0200.0300.0000.0140.0000.0200.0230.0000.0000.0000.0260.1880.0000.0020.0001.0000.0000.0000.0040.0430.0280.0000.0000.0000.0000.0480.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0440.0320.0070.0000.0000.0110.0000.0330.0000.1350.0000.0250.0000.0610.1060.0160.0000.0420.0000.0000.0000.0000.0190.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0470.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0251.0000.0680.0250.0000.000
zab_leg_020.0740.1090.0580.0360.0400.0430.0230.0650.0000.1320.1510.0810.0340.0000.0510.0280.0000.0000.0000.0000.0000.0350.2310.0000.0310.1031.0000.0200.0640.1200.0000.0600.0450.0230.0000.0310.0770.0850.0920.0740.0160.0430.0000.0000.0770.0810.0000.0000.0240.0510.0000.0000.0610.0820.0000.0000.0900.0270.1590.1200.0000.0700.0000.0000.0810.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0150.0000.0000.0000.0000.0000.0600.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0050.0590.0000.0100.0000.0681.0000.0140.0000.000
zab_leg_030.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.1710.0000.0750.0000.0000.0170.0000.0000.0000.0091.0000.0290.1070.0000.0000.0001.0000.0000.0000.1130.0000.0000.0950.1130.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0150.0000.0270.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0290.0520.0000.0000.0240.0000.1120.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.1140.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0550.0880.0410.0000.0000.0000.0250.0141.0000.0000.000
zab_leg_040.0440.0000.0000.0380.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0250.0000.0520.0000.0070.0000.0000.0170.0630.0000.0000.0001.0000.1780.0000.0000.0340.0000.1740.0000.0180.0000.0630.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0050.0000.0290.0000.0080.0000.1690.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
zab_leg_060.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0330.0180.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0810.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0160.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0380.0000.0000.0000.0000.0001.000

Missing values

2024-11-09T15:12:14.028882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T15:12:15.184075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-09T15:12:19.335500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDAGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_nFIBR_PREDSPREDS_TAHJELUD_TAHFIBR_JELUDA_V_BLOKOTEK_LANCRAZRIVDRESSLERZSNREC_IMP_IM_STENLET_IS
01771211.02.0NaN3.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN180.0100.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.04.70.0138.0NaNNaNNaN8.016.04.00.00.01.0NaNNaNNaN0.00.00.00.00.00.00.01.00.00.01.01.00.00.0000000000000
12551100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN120.090.00.00.00.00.00.00.04.01.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.50.0132.00.380.18NaN7.83.02.00.00.00.01.00.01.00.00.00.00.01.00.00.01.00.01.01.01.00.01.0000000000000
23521000.02.0NaN2.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0150.0100.0180.0100.00.00.00.00.00.00.04.01.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.00.0132.00.300.11NaN10.8NaN3.03.00.00.01.01.01.00.01.00.00.03.02.02.01.01.00.01.01.00.00.0000000000000
34680000.02.0NaN2.00.03.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0NaNNaN120.070.00.00.00.00.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.90.0146.00.750.37NaNNaNNaN2.00.00.01.0NaNNaNNaN0.00.00.00.00.00.00.00.00.01.01.01.00.00.0000000001000
45601000.02.0NaN3.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0190.0100.0160.090.00.00.00.00.00.00.04.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.50.0132.00.450.22NaN8.3NaN9.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.01.0000000000000
56641012.01.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN140.090.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.450.22NaN7.22.02.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.01.01.00.00.0100000000000
67701112.01.0NaN2.00.07.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0120.080.0120.080.00.00.00.00.00.00.00.00.03.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.300.11NaN11.15.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.0000000001000
78651011.02.0NaN2.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN145.095.00.00.00.00.00.00.00.00.02.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.50.0136.0NaNNaNNaN6.220.07.03.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.01.00.00.0000000000000
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